Sr. Product Specialist (T)

• *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