Forward Deployed Engineer
Archetype: Builder · Learner · Pair programmer
Role Summary
You are an on-the-ground builder who writes real production code inside client environments from day one. You come in hungry, move fast, and turn ambiguous problems into working prototypes within hours — not weeks. Your credibility comes entirely from working software, not slides.
What You Will Do
• Embed directly at client sites to prototype and deploy agentic AI workflows using frameworks such as LangGraph, CrewAI, AutoGen, or AWS Bedrock Agents — shipping working code, not slide decks.
• Build RAG pipelines end-to-end: chunking strategies, vector store configuration (Pinecone, pgvector, Weaviate), retrieval tuning, and response evaluation.
• Instrument LLM-powered applications with observability tooling (LangSmith, Braintrust, Arize) so clients can see exactly what their agents are doing in production.
• Participate actively in daily client stand-ups and technical reviews, communicating clearly about progress, blockers, and trade-offs with both engineers and business stakeholders.
• Rapidly iterate on prototypes based on user feedback — from zero to demo in 24–48 hours is the expectation, not the exception.
• Document deployment architectures, prompt engineering decisions, and integration patterns so knowledge persists after you rotate off an engagement.
• Contribute reusable agent templates and accelerators to Cognizant's internal AI toolkit between engagements.
Technical Foundation
• Strong Python; basic TypeScript / JavaScript
• REST API design and integration
• Git, CI/CD basics, containerisation (Docker)
• SQL and at least one cloud platform (AWS / Azure / GCP)
• Hands-on LLM experience (OpenAI, Anthropic, Gemini APIs)
GenAI / Agentic AI Requirements
• Has built at least one end-to-end RAG or agent application — personal projects count as strongly as work experience
• Understands prompt engineering, few-shot design, and chain-of-thought prompting
• Familiar with agentic orchestration concepts: tool use, memory, planning loops
• Knows how to evaluate LLM output quality — even informal logging or manual review frameworks
What Makes You Stand Out
• You have shipped something real with AI — a GitHub repo, a side project, a hackathon win — not just certificates
• You are comfortable being wrong in front of a client and pivoting immediately
You ask 'what does done look like?' before writing a single line of code
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。
Cognizant 是一家提供平等就业机会的雇主。在招聘过程中，您的申请和候选资格不会因种族、肤色、性别、宗教、信仰、性取向、性别认同、国籍、残疾、遗传信息、怀孕、退伍军人身份或任何其他受联邦、州或地方法律保护的特征而受到影响。
