Forward Deployed Engineer Archetype: Owner · Problem solver · Client partner Role Summary
You own an engagement end-to-end. You diagnose messy real-world problems, architect multi-agent solutions, build them in production, and leave clients materially better off — with a running system, not a proof of concept. The pod looks to you to set the technical direction and hold the client relationship. What You Will Do
- Lead small FDE pods (typically 2–3 engineers) embedded with a client for 8–16 week sprints, owning technical delivery from discovery through production launch.
- Translate ambiguous business objectives into concrete agentic AI architectures — defining agent roles, tool interfaces, orchestration patterns, memory strategies, and human-in-the-loop checkpoints.
- Design and implement multi-agent systems for complex enterprise workflows: document intelligence, process automation, decisioning pipelines, and AI-assisted knowledge work.
- Conduct rigorous evaluation: design eval suites, run red-teaming exercises, set acceptance criteria, and present evidence-based quality assessments to client engineering leads and executives.
- Navigate client-side security, IAM, data residency, and compliance constraints to deploy AI in regulated environments (BFSI, healthcare, manufacturing).
- Build trust with senior client stakeholders — running architecture reviews, leading technical workshops, and communicating trade-offs in plain business language.
- Feed deployment patterns and reusable components back into Cognizant's AI Market Unit asset library, accelerating future engagements.
- Mentor Jr. FDEs, pair on hard technical problems, and raise the floor of the whole pod. Technical Depth Required
- Production-grade Python; TypeScript / JavaScript for full-stack agent UIs
- Agentic frameworks: LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent
- Cloud-native deployment: Kubernetes, serverless, managed AI services
- Data engineering fundamentals: ETL, streaming (Kafka / Kinesis), vector and relational databases
- AI observability, guardrails, and safety tooling Client & Delivery Requirements
- Has owned at least one GenAI deployment from prototype to production in a real client or employer environment
- Comfortable presenting architecture decisions to VP-level technical and business stakeholders
- Experience running iterative delivery (sprint planning, retrospectives, change management basics)
- Domain knowledge in at least one of: BFSI, healthcare, supply chain, retail, or manufacturing What Makes You Stand Out
- You have rescued a stalled AI project — diagnosed why demos worked but production didn't, and fixed it
- You can tell a client 'that's the wrong use case' and redirect them to something that will actually deliver ROI
You treat evals as engineering, not an afterthought
