AI Engineering Lead

Job Summary

Required Qualifications

• •Lead end-to-end project delivery with clear governance and strong stakeholder communication
• •Mentor junior engineers and contribute to proposals and new business initiatives
• •Define what AI systems should and should not attempt, and communicate risks and tradeoffs transparently to clients
• •Design and build RAG systems, agentic frameworks, and LLM-powered solutions robust enough for production
• •Apply advanced prompt engineering techniques, including instruction design, few-shot sets, structured outputs, and tool/agent prompts
• •Lead feasibility assessments to select the right approach among prompting, RAG, fine-tuning, or classical ML
• •Design evaluation frameworks, including LLM-as-a-judge methods, custom metrics (recall@k, precision@k), and go/no-go gates
• •Run structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence rather than intuition
• •Identify and categorize model failure modes, including hallucinations, retrieval misses, and instruction-following errors
• •Build scalable inference infrastructure and CI/CD pipelines for AI/ML models
• •Automate the full MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining
• •Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability
• •Expert-level Python, strong Git practices, and experience with ML/LLM versioning
• •Solid cloud experience across AWS, Azure, or GCP (Azure preferred), plus containerization and orchestration
• •Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
• •Proven MLOps/LLMOps track record using tools such as MLflow, Weights & Biases, or similar
• •Practical evaluation design skills, including metrics, dataset curation, and structured experimentation
• •Experience with event-driven architectures, APIs, and microservices
• •Strong communication skills, equally comfortable engaging engineering teams and senior stakeholders
• •English: Advanced (required for effective communication with global teams)
• •6+ years of experience building and deploying AI solutions in production environments, with a strong track record across RAG, agentic systems, and MLOps/LLMOps

Desired Qualifications

• •Preferred: experience with the Databricks MLOps platform, LLM fine-tuning, building agentic GenAI systems, Infrastructure as Code, security and observability for AI services, a classical ML background, and open-source contributions