Forward Deployed AI Engineer

Job Summary

As a Forward Deployed AI Engineer and Technical Lead, you will define the technical strategy for a dedicated squad embedded within the business. Collaborating directly with stakeholders, you will gain a clear understanding of the problem and its purpose, then design, develop, deploy, and operate agentic AI systems that address the challenge end-to-end. This role demands hands-on development with sole responsibility for delivering impactful aviation solutions. You'll work closely with a Business Product Owner and AI Value Architect on a shared platform that enables your squad to independently integrate with client systems.

Accountabilities & Responsibilities

• Prioritize comprehending the business need and rationale before starting development by engaging with stakeholders, driving solutions from discovery through production.
• Architect, implement, deploy, and enhance enterprise-grade agentic AI applications tailored to authentic aviation use cases, utilizing agentic coding as a fundamental approach.
• Develop intelligent agents capable of multi-step reasoning, tool and API invocation, context management, exception handling, and supporting human oversight, functioning reliably at enterprise scale.
• Construct Retrieval-Augmented Generation (RAG) pipelines over enterprise knowledge assets including ingestion, chunking, embedding creation, vector search optimization, retrieval tuning, grounding, and source traceability.
• Integrate using MCP-based frameworks connecting agents to backend systems through REST/OpenAPI, webhooks, and event-driven designs with secure authentication, while also exposing reusable, clean, self-serviceable interfaces.
• Implement comprehensive LLM usage patterns encompassing tool invocation, schema-validated output, retries, fallback mechanisms, and guardrails.
• Ensure product quality from the outset by employing testing, evaluation, observability, logging, version control, and feedback loops focusing on reliability, accuracy, latency, security, and cost efficiency.
• Embed security principles, privacy safeguards, access control, audit functionality, responsible AI practices, and governance throughout all deployments.
• Assume end-to-end ownership of specific domain outcomes, establishing reusable AI patterns to enhance Etihad’s internal AI capabilities rather than outsourcing.
• Collaborate closely with the Business Product Owner, AI Value Architect, and other squads; advocate for alternative solutions when AI is not appropriate.
• Lead the squad’s agentic AI technical direction, establish standards, and make critical architecture and build-versus-buy decisions.
• Design multi-agent systems and inter-agent communication frameworks with robust evaluation methodologies, and lead engagements with external AI platforms and vendors while nurturing internal capabilities.
• Mentor and develop engineers on the team, uphold high standards of quality, security, and cost optimization across the squad.

Education & Experience

• Proven technical lead responsible for setting engineering direction in agentic AI development while maintaining hands-on involvement, combined with a business-oriented approach.
• Intense curiosity: a drive to deeply investigate problems, challenge assumptions, and understand airline operations thoroughly.
• Business-centric and people-focused perspective: understanding aviation’s human-centered nature and ensuring AI supplements rather than replaces humans; fluent English; skilled in working within diverse, multicultural teams.
• Minimum of 8 years developing production-quality software, including over 4 years experience with Generative AI, Large Language Models (LLMs), and applied machine learning, with at least 1 year of direct hands-on work in agentic AI as an early innovator, demonstrating history of directing and delivering agentic systems at scale.
• Practical knowledge or experience with Model Context Protocol (MCP) for connecting agents with tools, APIs, systems, and data sources.
• Strong proficiency in Python programming and solid familiarity with one or more of TypeScript/JavaScript, Java, or C#; expertise in modern engineering practices such as asynchronous programming, FastAPI, Pydantic, Git, CI/CD pipelines, testing frameworks, error management, and comprehensive logging.
• Experience working with at least one agent framework or enterprise AI platform, for example LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock, AgentCore, Google Vertex/Gemini.
• Familiarity with vector databases or search platforms such as Azure AI Search, pgvector, Pinecone, Weaviate, or OpenSearch.
• Experience integrating enterprise APIs, managed identities, webhooks, message queues, middleware, and deploying cloud-based containerized applications with monitoring and observability; sound judgment balancing latency, quality, cost, reliability, alongside security, privacy, responsible AI, and governance considerations.
• Valuable additional skills include aviation domain knowledge; background in classical machine learning and data science; full-stack development expertise covering user interfaces, frontends, APIs, and backend engineering.
• Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or related fields, or equivalently demonstrated experience; relevant certifications in cloud AI, Generative AI, agentic AI, or MLOps are advantageous.

Preferred Qualifications for this Position

• Experience designing and deploying AI agents for complex enterprise or logistics-heavy workflows such as supply chain, aviation, cargo operations, customer service, or contact centers.
• Skills in voice AI, email automation, CRM systems integration, workflow automation, or supporting multilingual agent capabilities.
• Expertise in creating golden test datasets, simulation-based testing, regression tests, and comprehensive agent evaluation frameworks.
• Design and implementation knowledge of multi-agent systems, agent-to-agent communication protocols, agent registries, or orchestration standards.
• Proven leadership in delivering solutions with external AI vendors, startups, or platforms while simultaneously building internal AI engineering capacity.

Career Growth

The natural progression leads to the role of AI Value Architect, where one manages the value journey of a cluster, continuing to collaborate closely with the development team.