Forward Deployed Engineer
Most engineers build inside a controlled codebase. This role is different. You deploy, integrate, and customise inside a client's actual environment — their data, their constraints, their workflows — and you own reaching working value quickly.
The problems you encounter are rarely clean. Scope shifts. Data is imperfect. Stakeholders want answers before the integration is stable. That is the job, and it suits engineers who prefer shipped outcomes to tidy abstractions.
You are also the technical face to the client. That means scoping problems clearly, explaining solutions without jargon, and earning trust through what you deliver — not what you promise. What you learn in the field comes back into the product and delivery playbook, so the work compounds.
Who thrives here
Engineers who want ownership of the full arc: problem, build, ship, operate. People who are comfortable walking into a room where the requirements are incomplete and leaving with a working plan. Those who find a backlog of tickets less satisfying than a customer whose system now works.
What you'll own
- Embed with client teams to understand their workflows, data, and the real problem behind the stated one.
- Deploy, integrate, and customise solutions — including AI and GenAI components — inside the client's environment.
- Build the integrations, data pipelines, APIs, and custom features that make a solution production-real.
- Rapidly prototype and iterate toward a working outcome under ambiguity and with imperfect data.
- Troubleshoot and operationalise what you ship; act as the primary technical contact for the client.
- Feed field learnings back into the product and delivery playbook.
What it takes
- 3–8+ years of hands-on software engineering (backend or full-stack).
- Strong coding ability in Python and/or Java, JavaScript, or Go.
- Real customer-facing ability: can scope a problem with a client and explain a solution clearly.
- Strong integration and API skills; comfort working with data.
- Working knowledge of at least one major cloud platform — AWS, Azure, or GCP.
- Bias to action, ownership, and comfort with fast iteration in ambiguous conditions.
Nice to have
- GenAI or LLM implementation experience — RAG, prompt engineering, LLM APIs, LangChain, LlamaIndex, vector databases.
- Background in solutions engineering, consulting, or a startup environment.
- Enterprise or BFSI domain exposure.
- Experience deploying software at customer sites or in regulated environments.
We respond to every application within 2 business days.