NC
AI Value Architect
NR Consulting - India
Location not stated
4 weeks ago
- AI
- RAG
- Vector Search
- MCP
- OpenAPI
- Webhooks
- Machine Learning
- Model Context Protocol
- Python
- TypeScript
- JavaScript
- Java
- C#
- FastAPI
- Pydantic
- Git
- CI/CD
- LangGraph
- Semantic Kernel
- CrewAI
- AutoGen
- OpenAI
- Foundry
- Amazon Bedrock
- Gemini
- Azure AI
- pgvector
- Pinecone
- Weaviate
- OpenSearch
- CRM
- AI/ML
- MLOps
4 weeks ago
Location: Abu Dhabi
Exp: 8+yrs
Job Description:
• 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).
• Preferred for this level:
o Experience building AI agents for complex enterprise or operations-heavy workflows (logistics, supply chain, aviation, cargo, customer operations or contact centre).
o Experience with voice AI, email automation, CRM integrations, workflow automation or multilingual agents.
o Experience designing golden test sets, simulation-based testing, regression testing and agent evaluation frameworks.
o Experience designing multi-agent systems and agent-to-agent communication patterns, agent registries or tool-orchestration standards.
o Experience leading delivery with external AI platforms, startups or vendors while building internal engineering capability.
• 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
AI Value Architect · NR Consulting - India