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
コグニザントについて
コグニザント（NASDAQ: CTSH）は、AI Builderおよびテクノロジーサービスプロバイダーとして、お客様にフルスタックのAIソリューションを構築することで、AI投資と企業価値を結ぶ架け橋となっています。業界、ビジネスプロセス、エンジニアリングに関する当社の深い専門知識を活かし、組織固有のビジネス環境をテクノロジー・システムに組み込みます。これにより、人間の可能性を最大限に引き出し、確かな成果を実現するとともに、急速に変化する世界においてグローバル企業が常に一歩先を行くための支援を行っています。 詳細については、cognizant.ai をご覧ください。
雇用に関する追加情報
本募集に記載されている報酬情報は、掲載日時点で正確なものです。Cognizantは、適用される法令に従い、いつでも本情報を変更する権利を留保します。
応募者は、対面またはビデオ会議による面接への参加を求められる場合があります。また、各面接の際に、現在有効な州政府または政府発行の身分証明書の提示を求められる場合があります。
Cognizantは機会均等雇用主です。応募および選考において、人種、肌の色、性別、宗教、信条、性的指向、性自認、国籍、障がい、遺伝情報、妊娠、退役軍人の地位、その他連邦法・州法・地方自治体の法律により保護されるいかなる特性に基づく差別も行いません。
