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
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
