AI Tech

Purpose of the Role

The AI Lead is a senior technical leadership role responsible for driving the end-to-end delivery of AI and Agentic AI solutions. You will own the technical direction of AI projects, lead a team of AI Engineers, and serve as the bridge between business stakeholders, solution architects, and engineering teams. You are accountable for solution quality, delivery cadence, and production readiness of AI workloads.

Key Accountabilities

Technical Leadership & Architecture

• Own the technical delivery of AI solutions from ideation through to production deployment
• Define and enforce solution architecture patterns including single-agent, multi-agent, and orchestration topologies
• Make technology selection decisions with cost, performance, and maintainability trade-offs
• Design and approve prompt architectures, retrieval strategies, and context assembly patterns
• Establish reference architectures and reusable design patterns for the AI engineering team

Delivery Management

• Lead AI projects through a stage-gated delivery model: Ideation → Framing → Design → Build → Test → UAT → Production
• Own design approval gates and ensure every deliverable meets agreed quality bars before stage exit
• Manage non-functional requirements (latency, cost-per-task, throughput) and ensure they are met at each gate
• Drive parallel workstreams (e.g., systems data readiness alongside agent build) to prevent schedule slip
• Coordinate cross-team dependencies with platform, MLOps, data, and business teams

Team Leadership

• Lead, mentor, and grow a team of AI Engineers across agent development, prompt engineering, and LLM integration
• Conduct code reviews and AI engineering quality checks
• Establish and enforce development standards, coding guidelines, and engineering best practices
• Foster a culture of experimentation, continuous learning, and knowledge sharing
• Manage team capacity, sprint planning, and delivery commitments

Agentic AI & LLM Expertise

• Design and oversee agentic orchestration solutions (multi-agent systems, state management, human-in-the-loop patterns)
• Lead prompt engineering strategy including system prompt design, few-shot patterns, chain-of-thought reasoning, and prompt versioning
• Architect Retrieval-Augmented Generation (RAG) pipelines including chunking strategies, embedding models, and retrieval quality evaluation
• Drive evaluation harness design: golden-set creation, grading rubrics, pass-bar thresholds, and CI-integrated eval suites