Our client is building frontier AI systems and is looking for an AI Engineering Lead who acts as the technical execution engine of an AI pod.

The role sits at the intersection of deep AI engineering and technical leadership. You design, build, and land production-grade AI systems with zero delivery drift — translating architecture into reliable, scalable, observable implementations.

You own engineering quality end to end: agentic workflows, pipelines, code standards, reviews, and delivery excellence across the whole pod.

• Implementation leadership: convert solution architecture into actionable engineering plans and execution sequences
• Agentic systems: architect multi-agent workflows, validator agents, tool calls, and RAG pipelines, and own agent reliability under real-world latency, cost, and drift constraints
• Engineering standards: enforce coding standards and architecture-aligned patterns, run code reviews, pair programming, and design reviews
• Deep problem solving: diagnose complex issues across agents, retrieval, data pipelines, APIs, and integrations — and make fast decisions under ambiguity
• Performance & observability: own latency, throughput, concurrency, and cost; ensure logging, metrics, tracing, drift detection, and failure handling
• DevOps excellence: make sure CI/CD pipelines support AI workloads, evaluations, and iterative releases across dev, test, stage, and prod
• Mentoring: grow AI engineers, ML engineers, QA, and integrators, and build scalable engineering capability for future pods

• Strong mastery of Python for AI services, pipelines, and APIs
• Hands-on agentic and RAG experience: embeddings, vector stores, retrieval optimization
• Production LLM integration — Azure OpenAI, Bedrock, Claude, and similar
• Model evaluation frameworks, safety, and guardrails in a production context
• System-level engineering: microservices, APIs, event-driven systems, data pipelines, and cloud architecture on AWS, Azure, or GCP
• DevOps depth: CI/CD pipelines, Git workflows, observability, telemetry
• 5+ years leading engineering teams or complex AI/ML delivery tracks in high-intensity, multi-disciplinary pods
• Mindset: high ownership, low ego, obsessive about quality and stability, natural mentor

• Ownership of frontier AI delivery — real production systems, not experiments
• Direct influence on architecture, engineering standards, and team culture
• A high-trust environment with genuine technical autonomy
• Close collaboration across UX, QA, architecture, and business
• A long-term role with strategic impact and a clear path to scale the pod

ASAP