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
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
申请人可能需要通过现场面试或视频会议的方式参加面试。此外，候选人在每次面试时可能需要出示其当前所在州或政府签发的有效身份证件。
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