AI Engineer Manager

As part of this role, you will be responsible for: Leading AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution. Designing and building robust RAG systems, agentic frameworks, and LLM-powered solutions suitable for production environments. Applying advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts. Leading feasibility assessments to determine the right technical approach, including prompting, RAG, fine-tuning, classical ML, or hybrid solutions. Designing evaluation frameworks for AI systems, including LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates. Running structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models. Identifying and categorizing model failures such as hallucinations, retrieval misses, instruction-following errors, and quality regressions. Building scalable inference infrastructure and CI/CD pipelines for AI and ML models. Automating the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement. Designing APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability. Mentoring junior engineers and contributing to proposals, solution design, and new business initiatives. The ideal candidate should have: 6+ years of experience building and deploying AI, ML, or data-driven solutions in production environments. Strong expertise in Python and solid Git practices. Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns. Practical experience with RAG components, including chunking, embeddings, retrieval, reranking, and evaluation. Strong understanding of prompt engineering techniques, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts. Proven experience designing evaluation strategies for AI systems, including metrics, dataset curation, structured experimentation, and quality gates. Experience with MLOps/LLMOps practices and tools such as MLflow, Weights & Biases, or similar platforms. Solid cloud experience with AWS, Azure, or GCP. Azure experience is preferred. Experience with containerization, orchestration, scalable inference, APIs, and microservices. Understanding of event-driven architectures and production-grade engineering practices. Ability to communicate clearly with engineering teams, senior stakeholders, and clients. A pragmatic approach to AI delivery, balancing innovation, reliability, cost, latency, and business value. Nice to have: Experience with Databricks MLOps platform. Experience with LLM fine-tuning. Experience building agentic GenAI systems. Experience with Infrastructure as Code. Knowledge of security and observability practices for AI services. Background in classical…