Forward Deployed AI Engineer

Job Title: Forward Deployed AI Engineer Company: Cognizant Job Category: Machine Learning Roles & Responsibilities: 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 client 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. Work Location: Dubai, Dubai, United Arab Emirates Availability: Full time

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. Key Skills: Machine Learning, Python Salary: Not disclosed