Job Summary :
As a Forward Deployed AI Engineer, Technical Lead you set the technical direction for a squad embedded in the business: you sit with stakeholders to understand the problem and the why, then design, build, deploy and run the agentic AI systems that solve it, end to end. This is a hands-on, build-first role with single-threaded ownership of a real aviation outcome, working alongside a Business Product Owner and an AI Value Architect, on a shared platform (paved road, MCP fabric, standards) that lets your squad self-serve against Etihad’s systems.
Accountabilities & Responsibilities
- Understand before you build. Start every problem with the business need and the why, working directly with stakeholders, then take the solution from discovery to production.
- Design, build, deploy and continuously improve enterprise-grade agentic AI applications for real aviation scenarios, using agentic coding as your default way of working.
- Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop, reliably and at enterprise scale.
- Design and implement RAG pipelines over enterprise knowledge: ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.
- Build MCP-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks and event-driven patterns with secure authentication, and expose your own work as clean, reusable, self-serviceable interfaces.
- Apply structured LLM patterns end to end: tool calling, schema-validated outputs, retries, fallbacks and guardrails.
- Own quality from day one: testing, evaluation, observability, logging, versioning and feedback loops for reliability, accuracy, latency, security and cost.
- Apply security, privacy, access control, auditability, responsible-AI and governance across every deployment.
- Take single-threaded ownership of a domain outcome (one owner, one result), and help establish reusable patterns that grow Etihad’s internal AI capability rather than renting it.
- Coordinate with your Business Product Owner, AI Value Architect and other squads; speak up when AI is not the right tool.
- Set the technical direction and standards for the squad’s agentic AI work, and make the key architecture and build-vs-buy calls.
- Design multi-agent and agent-to-agent systems and the evaluation frameworks that keep them reliable, and lead delivery with external AI platforms and vendors while building Etihad’s internal capability.
- Grow the engineers around you: mentor, review, and raise the bar on quality, security and cost across the squad.
Education & Experience
- We look for a technical lead who sets the engineering direction for agentic AI while still building, and combines that with a business-first mindset:
- Curiosity above all: you dig into problems, question assumptions and want to understand how the airline actually works.
- A business-first, human-centric mindset: aviation is made for humans, by humans, and AI supports people, it does not replace them. Fluent English, comfortable in a culturally diverse, international team.
- 8+ years building production-grade software, including 4+ years with GenAI, LLMs and applied ML and at least 1 year of hands-on agentic AI as an early adopter, with a track record of setting technical direction and shipping agentic systems at scale.
- Hands-on experience or strong working knowledge of MCP (Model Context Protocol) for connecting agents to tools, systems, APIs and data.
- Strong Python, with strong knowledge of at least one of TypeScript / JavaScript, Java, or C#, and modern engineering practice: async programming, FastAPI, Pydantic, Git and CI/CD, testing, error handling and logging.
- Practical experience with at least one agent framework or enterprise AI platform (e.g. LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock
- AgentCore, Google Vertex/Gemini) and with a vector database or search platform (e.g. Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch).
- Experience integrating enterprise systems (APIs, managed identities, webhooks, queues, middleware) and deploying on cloud with containers, monitoring and observability; sound judgement on the trade-offs of latency, quality, cost and reliability, and on security, privacy, responsible AI and governance.
- Strong assets: aviation or airline domain knowledge; a background in classical machine learning and data science; and classical full-stack development (interfaces, frontends, APIs, backend engineering).
- Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage.
Preferred for this level:
- Experience building AI agents for complex enterprise or operations-heavy workflows (logistics, supply chain, aviation, cargo, customer operations or contact centre).
- Experience with voice AI, email automation, CRM integrations, workflow automation or multilingual agents.
- Experience designing golden test sets, simulation-based testing, regression testing and agent evaluation frameworks.
- Experience designing multi-agent systems and agent-to-agent communication patterns, agent registries or tool-orchestration standards.
- Experience leading delivery with external AI platforms, startups or vendors while building internal engineering capability.
Growth path: the natural next step is AI Value Architect, owning a cluster’s value journey while still building alongside the team.
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。
