Hire Forward Deployed Engineers

Hire Forward Deployed Engineers (FDEs) who embed inside your team — your Slack, your Jira, your codebase, your customer conversations — and own AI delivery from the first discovery call through production rollout and post-launch iteration. Not consultants who leave a slide deck. Not agencies that ship to a spec. Not staff augmentation picking tickets from your backlog. FDEs own the outcome.

  • Experienced
    Developers

  • Projects
    Delivered

  • Industries
    Served

  • Countries We
    Served

Quick Answer

What is a Forward Deployed Engineer (FDE)?

A Forward Deployed Engineer is an AI engineer who embeds inside your team to own AI delivery from discovery through production and post-launch iteration. FDEs sit in your Slack, your Jira, your customer interviews — not behind a project manager. They make product decisions, ship to production, monitor real usage, and iterate. O Clock Software's FDEs are onboarded in 48 hours under NDA, with full IP ownership and end-to-end delivery ownership across quarters.

Recognized & Reviewed On

... ... ... ... ...
▲ What An FDE Actually Does

Nine things a Forward Deployed Engineer does that other engineering roles don't

The Forward Deployed Engineer model was pioneered by Palantir and adopted by Anthropic, OpenAI, Scale AI, and every AI company that ships production systems to serious customers. It's a specific engineering discipline that sits between consulting, product engineering, and customer success — and it looks fundamentally different from any of them in day-to-day work.

01

Runs Discovery, Not Just Implementation

An FDE sits in your customer interviews, defines the problem, scopes what to build, and validates assumptions before writing code. The engineer participates in figuring out WHAT to build — not just how to build a pre-defined spec.

Customer interviews · Problem framing · Assumption validation
02

Owns Product Decisions

What features ship first, what to cut, when good is good enough, when to pivot the approach. FDEs make product decisions with your product team — not wait for a spec that carries assumptions past their expiration date.

Product scoping · Trade-off analysis · Priority calls
03

Embeds In Your Environment

Your Slack, Jira, GitHub, cloud accounts, monitoring dashboards, customer conversations. No "we'll set up a status call every Tuesday" — the FDE works inside your team's actual daily rituals, with your tools, on your incident channels.

Your Slack · Your Git · Your cloud · Your rituals · Your on-call
04

End-to-End Delivery, No Handoff

Discovery → design → build → deploy → iterate. The same engineer stays across every stage. No spec-to-implementation handoff between teams. No "the AI team says the frontend team needs to do X." One person owns the whole chain.

Discovery → Design → Build → Deploy → Iterate
05

Production Ownership Through Launch

The FDE doesn't hand off to "another team" at deployment. They ship it, they monitor it, they debug it at 2am when it breaks. Production ownership is where AI systems either become products or become abandoned experiments.

Deploy · Monitor · Debug · On-call · Post-launch fixes
06

Iterates Based On Real Usage

Watches metrics after launch, reviews eval scores, listens to customer feedback, and iterates on prompts, retrieval strategy, model choice, or architecture based on what actually happens in production — not what the initial plan predicted would happen.

Metric review · Eval-driven iteration · Real-usage tuning
07

Navigates Compliance & Security

Works directly with your security, legal, and compliance stakeholders to clear enterprise procurement. SOC 2, HIPAA, GDPR, EU AI Act, data residency, DPA reviews — an FDE handles these conversations instead of stalling the project when the security questionnaire arrives.

SOC 2 · HIPAA · GDPR · EU AI Act · DPA · Enterprise procurement
08

Trains Your Team Through Delivery

Documents architecture decisions, mentors your internal engineers, sets up patterns your team can extend, and leaves the codebase in a shape your team can own. Knowledge transfer is baked into every engagement — not a phase after the "real work" ends.

Architecture docs · Team enablement · Pattern documentation
09

Long-Term Partnership

FDE engagements run across quarters and years, not weeks. Roadmap partnership, quarterly reviews, continuous iteration on the AI features shipped in earlier engagements. The relationship compounds — the FDE gets deeper context every quarter.

Quarterly reviews · Roadmap partnership · Multi-year relationship
▲ Four Engagement Models

FDE vs. Consultant vs. Agency vs. Staff Augmentation

Buyers pick between four engagement models when they need AI work done. Each is right for a specific situation — and none of them is a replacement for the others. Here's the honest framework we use in discovery calls to help buyers choose the model that fits their actual situation.

FDE ★ Ownership

Forward Deployed Engineer

Embedded engineer · end-to-end delivery · owns outcomes across quarters
  • Works inside your environment
  • Owns discovery + build + deploy + iterate
  • Product decisions + engineering execution
  • Production ownership through launch
  • Long-term partnership across quarters
Best For You need AI shipped end-to-end with someone who owns the outcome — not the task list.
Consultant

Consultant

Advises · doesn't build · time-boxed deliverable · leaves after report
  • Gives recommendations and strategy
  • Produces roadmaps and slide decks
  • Doesn't write production code
  • Time-boxed engagement (weeks)
  • Leaves after the deliverable
Best For You need strategic direction and haven't decided WHAT to build yet.
Agency

Agency

Spec-driven delivery · self-contained team · handoff at completion
  • Works to your requirements document
  • Own contained delivery team
  • Fixed scope and timeline
  • Handoff to your team at completion
  • Rarely stays for iteration
Best For You know exactly what you want built. You need it built to spec.
Staff Aug

Staff Augmentation

Extra hands · your direction · picks from your backlog · you own outcomes
  • Picks up tasks from your backlog
  • Works in your codebase
  • You define what they do daily
  • You own product decisions
  • You own outcomes
Best For You have a plan, a spec, and a backlog — you just need more capacity.
Still not sure which engagement model fits your project? Book a free 30-minute call. Our engagement lead walks through your project scope, team situation, timeline, and internal capacity — then recommends FDE, consultant, agency, or staff aug honestly. If you already know the specific technology stack you need built (RAG, generative AI, AI in a mobile app, multi-tenant AI SaaS), see the capability-specific pages — Hire RAG Developers, Hire Generative AI Developers, Hire AI Mobile App Developers, or Hire AI SaaS Developers. FDE is the engagement model that delivers across those capabilities.
▲ Why O Clock Software

What sets our Forward Deployed Engineers apart

The FDE model only works when the engineer has the right combination of AI depth, product sense, customer intimacy, and production discipline. Any three without the fourth produces something else — a consultant, an implementer, a customer success manager, or a research engineer. Our FDEs are hired and trained for all four.

FDE model designed for AI, not general software

Our FDEs are specifically AI engineers — LLM integration, RAG systems, agentic workflows, evals, fine-tuning, multimodal. Not generalist software engineers who added "AI" to their resume. That AI depth combined with FDE-style embedded delivery is where production AI systems actually ship.

Discovery through deployment ownership

The same engineer runs customer interviews, writes the code, deploys to production, watches the metrics, and iterates on real usage. No handoffs, no "we shipped v1, please engage a new team for v2." Continuity is where AI features either mature into products or stall as prototypes.

Enterprise security navigation experience

SOC 2, HIPAA, GDPR, EU AI Act, DPA reviews, data residency constraints, procurement questionnaires. Our FDEs have cleared enterprise security review at Fortune 500s, regulated industries, and government adjacencies — and know how to answer the questions procurement teams actually ask.

Long-term partnership, not one-shot

FDE engagements at O Clock Software run across quarters and years — not weeks. Roadmap partnership, quarterly reviews, continuous iteration on features shipped in earlier engagements. The FDE gets deeper context every quarter, which compounds into better product and engineering decisions.

▲ Why Hire From Us

Advantages of hiring Forward Deployed Engineers from O Clock Software

Six concrete reasons enterprises across India, Singapore, the US, Malaysia, and KSA choose our FDEs for embedded AI delivery.

Embedded from day one

Our FDE joins your Slack, Jira, GitHub, cloud accounts, standups, and customer interviews within 48 hours. Not "we'll set up a weekly status call." Actual embedded work inside your team's rituals, tools, and communication channels.

1

End-to-end delivery ownership

Discovery through deployment through iteration — the same engineer stays across every stage. No handoffs between teams. No "the AI person built v1, but for v2 we need to onboard someone new." Continuity is the foundation of FDE work.

2

Product decisions + engineering execution

FDEs make product calls with your product team — what to ship first, what to cut, when to pivot. Combined with the engineering execution to actually build and ship it. Not a hand-off between a product manager who decides and an engineer who builds.

3

Enterprise compliance navigation

SOC 2, HIPAA, GDPR, EU AI Act, DPA reviews, procurement questionnaires. Our FDEs have cleared enterprise security review across regulated industries — and know how to answer the questions security teams actually ask, not just the ones in the RFP template.

4

Knowledge transfer, not lock-in

Architecture documentation, internal engineer mentorship, pattern documentation, and codebase left in a shape your team can extend. FDEs are hired to make your team stronger — not to become the only people who can maintain what they built.

5

Flexible engagement, no lock-in

Full IP ownership signed before kickoff. Source code, prompts, models, and documentation in your repository from day one. Exit with [15/30]-day notice at any point. No long-term contractual lock-in.

6
▲ The Honest Comparison

FDE vs. Consultant vs. Agency vs. Staff Aug — the detail

A row-by-row comparison of how each engagement model handles the concerns that matter most in AI delivery — expanded from the four-card summary above.

ConsultantAgencyStaff AugFDE (O Clock)
Runs customer discoveryYes, then leavesNo — works to specNo — works to your backlogYes — throughout
Owns product decisionsAdvises, doesn't ownNo — you specNo — you decideYes — with your team
Writes production codeRarelyYes, to specYes, from your backlogYes, end-to-end
Embeds in your environmentTheir environmentTheir environment mostlyYesYes — your Slack, Git, cloud
Deploys to productionNoDelivers, hands offYes, if in the backlogYes — owns launch
Iterates post-launchNo — engagement endedRarely — new SOW requiredYes, if in the backlogYes — continuous
Handles compliance / security reviewAdvisesRarely engagedYou handleYes — with your legal/security
Onboarding time2–4 weeks4–8 weeks1–3 weeks48–72 hours
Engagement length4–12 weeks3–6 monthsOngoingMulti-quarter / multi-year
NDA & IP ownershipStandardStandardStandardFull — signed before kickoff
Replacement guaranteeNoneContract renegotiationMarketplace-dependentFree, within trial
▲ What FDEs Deliver

FDE engagement services O Clock Software delivers

The services below describe the work an FDE actually does through an engagement — the activities you should expect your embedded engineer to run, not just the technology categories they touch.

FDE Discovery & Scoping

Free 30-min consultation to scope your AI opportunity, define success criteria, identify blockers, and validate that FDE is the right engagement model — versus consultant, agency, or staff aug. Honest recommendation.

Customer Interview Facilitation

FDE sits in on your customer discovery interviews, defines what to build based on real user pain, and validates assumptions before writing code. The product decisions get made with your product team — not for you in a slide deck.

Embedded Product Development

Works inside your Slack, Jira, GitHub, cloud accounts, and standups. Participates in product prioritization, sprint planning, and incident response. Not "the vendor" — actual team member behaviour for the duration of the engagement.

End-to-End AI Feature Delivery

Discovery → architecture → prototype → build → deploy → iterate. The same FDE owns every stage. No spec-to-implementation handoff. AI features ship as products with production ownership through launch and beyond.

Production Deployment Ownership

The FDE deploys, monitors, debugs, and is on-call for what they built. Not "we shipped v1 to your team, good luck." Production ownership is where AI systems either mature into products or fail silently over the next month.

Post-Launch Iteration & Evals

Reviews metrics, eval scores, and customer feedback. Iterates on prompts, retrieval strategy, model selection, chunking, re-ranking, or architecture based on real production behaviour — not what the initial plan predicted would happen.

Enterprise Compliance Navigation

Works with your security, legal, and compliance teams to clear procurement. SOC 2, HIPAA, GDPR, EU AI Act, DPA reviews, data residency reviews. Answers the questions your buyers' security teams actually ask, not just the RFP template.

AI Architecture & System Design

End-to-end architecture — data ingestion, model routing, retrieval pipeline, evaluation harness, observability, tenant isolation, deployment topology. Coherent design that survives real-world scale, not a wire diagram to hand off.

Team Enablement & Documentation

Architecture decision records, runbooks, eval playbooks, prompt libraries, and mentorship for your internal engineers. FDEs are hired to make your team stronger — not to become the only people who can maintain what got built.

Multi-Quarter Roadmap Partnership

Quarterly business reviews, product roadmap partnership, continuous iteration across quarters and years. The FDE relationship compounds — deeper context every quarter, better product and engineering decisions over time.

Cross-Team Coordination

Works across your engineering, product, design, sales, and customer success teams. Facilitates the conversations AI features need to actually ship — the ones a task-focused contractor would never have because they weren't in the room.

Executive Stakeholder Reporting

Progress reviews, quarterly demos, board-level updates, ROI reporting when appropriate. FDEs can present to your executive team, board, or investors — because they own the outcome and understand the story behind the metrics.

▲ Flexible Engagement

Choose how you want to engage a Forward Deployed Engineer

Six flexible engagement models designed to match your project stage, team situation, and risk tolerance — from single embedded FDE to fully-owned AI product pods.

★ Most Popular
1

Single Embedded FDE

One Forward Deployed Engineer embedded full-time into your team. Owns end-to-end delivery of a defined AI initiative. Multi-quarter default engagement length.

  • Full-time embedded (160 hrs/month)
  • End-to-end delivery ownership
  • Multi-quarter engagement
  • Best for defined AI initiatives
2

Dedicated FDE Pod

2–5 FDEs plus a lead FDE, working as a self-contained delivery team on a larger AI initiative — RAG platform, agentic system, multi-modal product.

  • Self-contained pod
  • Includes lead FDE
  • Larger AI initiatives
  • Multi-quarter engagement
3

Part-Time FDE

80 hours/month embedded — ideal for iteration on shipped AI features, ongoing evals maintenance, or supplementing an internal team's AI work.

  • Half-time allocation
  • Continuous engagement
  • Flexible scheduling
4

Discovery Sprint

2–4 week focused discovery engagement — customer interviews, problem framing, technical scoping, and a recommended path forward. Then decide whether to continue with a full FDE engagement.

  • Time-boxed engagement
  • Discovery deliverable
  • Low commitment
5

Compliance-Cleared FDE

FDEs pre-cleared for HIPAA, SOC 2, or GDPR-sensitive engagements — with the paperwork and process already in place. Faster procurement, faster start.

  • Pre-cleared compliance
  • Faster procurement
  • Regulated industries
6

Executive-Sponsored FDE

FDE reporting to a specific executive stakeholder — CTO, VP Eng, Chief AI Officer — with regular executive check-ins and strategic reporting cadence built into the engagement.

  • Exec-sponsored reporting
  • Strategic alignment
  • Board-visible engagements
▲ Full-Stack AI Capability

Technologies our FDEs work fluently with

FDEs are technology-agnostic by design — the right stack depends on what your project actually needs. Below is the range of AI infrastructure, model providers, evaluation platforms, and application layers our FDEs deliver across.

▲ LLM Providers & Foundation Models

OpenAI GPTAnthropic ClaudeGoogle GeminiAWS BedrockAzure OpenAICohereMistralLlama · Qwen · DeepSeek

▲ RAG & Retrieval

PineconeWeaviateQdrantpgvectorMilvusCohere RerankBGE RerankerHybrid BM25+Vector

▲ Agentic Frameworks

LangGraphCrewAIAutoGenMCPOpenAI AssistantsVercel AI SDKDSPyInstructor

▲ Evals & Observability

RagasTruLensDeepEvalPhoenix (Arize)LangSmithBraintrustLangfuseHelicone

▲ Cloud & DevOps

AWSGCPAzureKubernetesTerraformGitHub ActionsModalVercel

▲ Application Layer

Next.jsReactTypeScriptPythonNode.jsFastAPITailwindshadcn/ui

▲ Data & Ingestion

UnstructuredLlamaParseFirecrawlAirbyteFivetrandbtPostgresSnowflake

▲ Enterprise Integrations

SalesforceHubSpotSnowflakeDatabricksSAML / SCIMAuth0WorkOSVault

How an FDE engagement starts

...
...

1. Understanding the Requirements

The first step is to understand the client's specific needs and requirements, including project goals, budget, timelines, and technical requirements.

...

2. Selecting the Right Developers

Based on the requirements, our HR team selects the best-fit developers from the talent pool with the right skills, experience, and cultural fit.

...

3. Technical Assessment

After the initial screening process, the shortlisted developers are tested on their technical skills, including coding tests, problem-solving tasks, and other assessments.

...

4. Interview

The selected candidates are interviewed by the hiring team to assess their communication skills, work ethics, and cultural fit with the company.

...

5. Onboarding and Training

Once the candidates are selected, they go through an onboarding and training process to ensure they understand the company's culture, policies, and development processes.

...

6. Continuous Monitoring and Feedback

Our project management team regularly monitors the progress of the project and provides continuous feedback to ensure that the client's requirements are met.

▲ Where FDEs Matter Most

Industries where the FDE model delivers

Some industries need embedded, end-to-end AI delivery more than others. Regulated verticals, complex enterprise buyers, and specialized workflows are where the FDE model dramatically outperforms consultants, agencies, and staff aug alternatives.

Enterprise SaaS

Embedded FDE inside your engineering team ships AI features that survive enterprise procurement, multi-tenant isolation, and long sales cycles.

Financial Services

Compliance-heavy AI delivery — KYC, fraud detection, advisor copilots, regulatory Q&A — where embedded expertise clears procurement faster than external agencies can.

Healthcare & Life Sciences

HIPAA-scoped AI systems where FDEs work alongside your clinical, compliance, and IT teams to ship features that pass audit the first time.

Legal & Professional Services

Specialized workflows, deep customer collaboration, citation-grounded AI. FDEs sit in on client interviews and iterate on evals from real matter data.

Manufacturing & Industrial

Domain-specific AI — quality inspection, supply chain, maintenance — where embedded FDEs learn the physical operation and ship AI that fits.

Government & Public Sector

Long procurement cycles and strict data-residency requirements — the FDE model handles the extended engagement lifecycle better than any alternative.

Logistics & Supply Chain

Real-time systems, dispatch, tracking, forecasting — AI features that need embedded engineering to iterate on real operational data over months.

Regulated Vertical?

FDEs work best in high-stakes, high-context industries. Let's talk about yours.

▲ Common Questions

Frequently asked questions

Optimized for AI answer engines (ChatGPT, Perplexity, Google AI Overviews). Wrapped in FAQPage schema for SEO.

What is a Forward Deployed Engineer (FDE)?
A Forward Deployed Engineer (FDE) is an AI engineer who embeds inside a customer's team to own AI delivery end-to-end — from customer discovery through production deployment and post-launch iteration. The FDE model was pioneered by Palantir and adopted by Anthropic, OpenAI, Scale AI, and other AI companies that ship serious production systems. FDEs sit in customer interviews, make product decisions with the customer's product team, write production code, deploy to production, monitor real usage, and iterate — all as a single continuous engagement, not a series of handoffs between separate teams.
What's the difference between an FDE and a consultant?
A consultant advises — they produce recommendations, strategy documents, and roadmaps, and typically leave after a defined engagement of a few weeks to a few months. Consultants rarely write production code and rarely own outcomes past the deliverable. An FDE embeds and builds — they participate in customer discovery, make product decisions, write and ship production code, own deployment, and stay through iteration across quarters. Consultants tell you what to do; FDEs do it with your team.
What's the difference between an FDE and staff augmentation?
Staff augmentation adds capacity to your team — an engineer picks up tasks from your existing backlog under your direction. You define what they do daily, you own product decisions, and you own outcomes. Staff aug is capacity, not ownership. An FDE takes ownership of a defined AI initiative — including customer discovery, product decisions, technical scoping, execution, deployment, and iteration. The FDE participates in defining what to build, not just building what you spec. Staff aug is right when you have a plan and need more hands; an FDE is right when you need someone who owns the outcome end-to-end.
How does an FDE work with our team day-to-day?
The FDE joins your Slack, Jira or Linear, GitHub or GitLab, cloud accounts (as a named user in your IAM), monitoring dashboards, and standups within 48 hours of engagement start. They participate in sprint planning, incident response, product prioritization, and customer interviews — as a team member for the duration of the engagement, not as an external vendor communicating through weekly status calls. You should experience the FDE as part of your team, using your tools and rituals.
Does an FDE make product decisions?
Yes — that's a defining feature of the FDE model. FDEs work with your product team to decide what features ship first, what to cut, when good is good enough, and when to pivot the technical approach. Product decisions get made in the room where they matter, with the person who will actually build the outcome — not handed down through a spec that carries assumptions past their expiration date. This makes FDE engagements more valuable but also requires that your product team is comfortable making decisions collaboratively.
Does an FDE own deployment to production?
Yes. FDEs own production deployment — infrastructure setup, CI/CD, monitoring, incident response, and on-call rotation for what they built. This is the defining discipline that separates FDEs from research engineers or prototype engineers. Production ownership is where AI systems either mature into products used by real customers or slowly fail as prototypes without a clear owner. FDEs stay through launch, first-week monitoring, first-month iteration, and beyond.
How does an FDE handle enterprise security reviews?
FDEs at O Clock Software have cleared enterprise security reviews across regulated industries — including SOC 2 (customer and internal), HIPAA-adjacent healthcare projects, GDPR-scoped EU customer deployments, EU AI Act risk classifications, and DPA reviews. The FDE participates in security questionnaires, answers architecture questions directly to the customer's security team, and works with legal on data processing agreements. This is significantly faster than the pattern where external agencies bounce questions back to a project manager and lose two weeks per procurement round.
Can an FDE work with our existing engineering team?
Yes — that's the standard mode. FDEs are designed to augment and elevate your existing engineering team, not replace it. Typical FDE engagements pair the embedded engineer with your internal team through the delivery cycle — the FDE brings AI-specific depth (LLMs, RAG, agentic systems, evals), your team brings domain and product knowledge, and the two combine on the actual delivery. Knowledge transfer and mentorship are baked into every engagement — your team should be stronger at the end than at the start.
What if we don't know what to build yet — can an FDE help with that?
Yes. The Discovery Sprint engagement (2–4 weeks) is designed for exactly this situation. An FDE runs a focused discovery phase — customer interviews, problem framing, technical feasibility scoping, competitive landscape review — and delivers a recommended path forward including what to build, what to skip, technical architecture at a high level, and rough timeline. After the sprint you can decide whether to continue with a full FDE engagement, engage a different model (agency, staff aug), or pause. This lets you use FDE expertise to scope the problem before committing to build it.
How long is a typical FDE engagement?
FDE engagements at O Clock Software are designed as multi-quarter or multi-year relationships, not week-long sprints. The value of the FDE model compounds — the FDE's context on your product, customers, codebase, and team gets deeper every quarter, which leads to better product and engineering decisions over time. Typical starting engagements run 2 to 4 quarters, with most extending to multi-year partnerships. Discovery Sprints (2–4 weeks) are the exception, designed as a low-commitment entry point.
Do you have FDEs with specific vertical expertise?
Yes. FDEs are matched to engagements based on vertical experience — healthcare (HIPAA-aware, EHR-adjacent), financial services (KYC, fraud, advisor tools), legal (citation-grounded workflows, contract analysis), enterprise SaaS (multi-tenant, BYOK, BYOC), manufacturing (industrial data, quality inspection), and government-adjacent regulated engagements. The FDE profile shortlist you receive after the discovery call is filtered on vertical relevance, prior compliance scope, and AI-specific expertise.
How do I hire Forward Deployed Engineers from O Clock Software?
Hiring FDEs from O Clock Software takes three steps: a free 30-minute discovery call to scope your AI initiative and validate FDE fit (versus consultant, agency, or staff aug), shortlisted FDE profiles delivered within 48 hours with matched vertical experience and prior embedded engagement references, and a risk-free paid trial before full embed. The entire process typically completes within 5 to 7 working days, from first contact to an FDE embedded in your team.
Can I engage an FDE on a part-time basis?
Yes. O Clock Software offers Part-Time FDE engagements at 80 hours per month — designed for iteration on shipped AI features, ongoing evals maintenance, or supplementing an internal team's AI work without needing full-time capacity. Part-time FDEs work with the same embedded discipline as full-time engagements — your Slack, your standups, your codebase — just at half the weekly hours.
Will my FDE work in my time zone?
Yes. With offices in Chennai, Singapore, Florida, Kuala Lumpur, and Riyadh, O Clock Software provides 4 to 6 hours of daily working overlap with every major global region — including EST, PST, GMT, CET, GST, SGT, and AEDT. Embedded work requires meaningful time-zone overlap to enable participation in your team's standups, customer interviews, and incident response. FDEs are matched to engagements partially on time-zone fit for exactly this reason.
Who owns the IP — including code, prompts, and architecture documentation?
The client owns 100% of source code, prompts, fine-tuned model weights, architecture documentation, eval suites, runbooks, and all derivative materials produced by the FDE during the engagement. Everything lives in your GitHub or GitLab repository from day one. Cloud accounts and LLM provider accounts are owned by your organization — the FDE deploys into your accounts, never their own. NDA and IP transfer agreements are signed before the FDE joins your Slack, before any code is written, and before any architecture is scoped.
What if my FDE isn't the right fit?
O Clock Software offers a free FDE replacement guarantee within the trial period. If the FDE doesn't meet your technical bar, communication standard, or culture fit, we replace them as part of the trial guarantee. The replacement FDE is onboarded within 5 to 7 working days with full handover documentation — architecture decisions, discovery notes, roadmap plans, and codebase context — so continuity of the engagement is preserved through the transition.
Does O Clock Software sign NDAs before FDE engagement discussions?
Yes. O Clock Software signs mutual NDAs before any project conversation that involves your business logic, customer data, intellectual property, AI roadmap, or strategic technology direction. For regulated industries — healthcare, financial services, legal, government, life sciences — we also sign data processing agreements, Business Associate Agreements where HIPAA applies, and comply with applicable regional data protection regulations before the FDE joins your environment.
Where is O Clock Software located?
O Clock Software is headquartered in Chennai, Tamil Nadu, India, with offices in Singapore, Florida (United States), Kuala Lumpur (Malaysia), and Riyadh (Saudi Arabia). Our Forward Deployed Engineers are based primarily in the Chennai office, serving clients across Asia, North America, the Middle East, Europe, and Australia through embedded engagements aligned to client time zones.
How can I get started with hiring a Forward Deployed Engineer?
Start with a free 30-minute consultation. Email sales@oclocksoftware.com, call +91-44-42089942, or message us on WhatsApp. Share your AI initiative context — the problem you're trying to solve, your current engineering situation, timeline, and any compliance scope. We'll validate whether FDE is the right engagement model (versus consultant, agency, or staff aug), send matched FDE profiles within 48 hours, and arrange interviews on your schedule.

Ready to hire an engineer who owns the outcome, not just the code?

Schedule a free 30-minute consultation with our engagement lead. Get an honest recommendation on whether FDE fits your project, matched FDE profiles within 48 hours, and onboard a Forward Deployed Engineer into your team within a week.