Our client is building frontier AI systems and is looking for an AI Engineering Lead / Development Lead who acts as the technical execution engine of an AI pod.
This role sits at the intersection of deep AI engineering and strong technical leadership. You will design, build, and land production-grade AI systems with zero delivery drift, translating architecture into reliable, scalable, and observable implementations.
You own engineering quality end-to-end: agentic workflows, pipelines, code standards, reviews, and delivery excellence across the pod.
Your Mission
- Convert AI architecture into clear engineering plans and execution sequences
- Lead the delivery of high-quality, production-ready AI systems
- Ensure every deployment is safe, observable, resilient, performant, and cost-aware
- Set engineering standards and mentor teams to deliver consistently at scale
Responsibilities
1. AI Implementation Leadership
- Translate Solution Architecture into actionable engineering plans
- Lead development of AI capabilities: agentic workflows, retrieval systems, pipelines, evaluators, integrations
- Maintain technical oversight of code, data flows, and services across the pod
2. Agentic Systems & AI Feature Development
- Architect and build multi-agent workflows, validator agents, tool calls, and RAG pipelines
- Implement prompts, behaviors, evaluation harnesses, and experiment instrumentation
- Own reliability of agent behavior under real-world constraints (latency, cost, drift, precision)
3. Code Quality & Engineering Standards
- Enforce coding standards and architecture-aligned patterns across AI and full-stack code
- Conduct code reviews, pair programming, debugging, and design reviews
- Ensure compliance with non-functional requirements (performance, reliability, security, auditability)
4. Unblocking & Deep Problem Solving
- Diagnose complex issues across agents, retrieval, data pipelines, APIs, and integrations
- Make fast, high-quality engineering decisions under ambiguity
- Identify delivery risks early and resolve them before sprint impact
5. Performance, Reliability & Observability
- Own runtime performance: latency, throughput, concurrency, cost
- Ensure logging, metrics, tracing, drift detection, and failure handling
- Partner with AI Ops on deployments, monitoring, rollback, and recovery
6. DevOps & Deployment Excellence
- Ensure CI/CD pipelines support AI workloads, evaluations, and iterative releases
- Maintain environment consistency across dev / test / stage / prod
- Support integration testing, stabilization, and release readiness
7. Mentoring & Team Development
- Mentor AI engineers, ML engineers, QA, and integrators
- Build scalable engineering capability for future pods
- Promote a culture of clean code, clarity, ownership, and craftsmanship
Required Skills & Experience
AI & Engineering Expertise
- Strong mastery of Python for AI services, pipelines, and APIs
- Hands-on experience with agentic systems, RAG, embeddings, vector stores, retrieval optimization
- Production integration of LLMs (Azure OpenAI, Bedrock, Claude, etc.)
- Experience with model evaluation frameworks, safety, and guardrails
- Solid understanding of cloud architectures (AWS, Azure, GCP)
System-Level Engineering
- Experience with microservices, APIs, event-driven systems, and data pipelines
- Ability to balance architectural purity with real-world delivery constraints
DevOps & Reliability
- Strong knowledge of CI/CD pipelines, Git workflows, observability, telemetry
- Proven ability to debug reliability issues across AI, data, and API layers
Leadership & Delivery
- 5+ years leading engineering teams or complex AI/ML delivery tracks
- Experience delivering in high-intensity, multi-disciplinary pods
- Strong collaboration across UX, QA, architecture, and business teams
Mindset
- High ownership, low ego
- Obsessive about quality, clarity, and stability
- Thrives in ambiguity and solves problems fast
- Natural mentor and technical authority
What the Role Offers
- Ownership of frontier AI delivery, not just experimentation
- Direct influence on architecture, standards, and engineering culture
- Work on real production systems, not demos
- High-trust environment with strong technical autonomy
- Long-term role with strategic impact
