AI Engineering Manager

Build scalable inference infrastructure and CI/CD pipelines for AI/ML models that support rapid iteration and reliable deployment

Automate the full MLOps/LLMOps lifecycle: tracking, versioning, deployment, monitoring, and retraining across the team

Design APIs, microservices, and orchestration layers optimised for latency, cost, and reliability

Lead infrastructure decisions that balance technical excellence with business efficiency

7+ years building and deploying AI solutions in production environments

2+ years of direct team leadership or technical management experience

Expert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deployment

Solid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and orchestration knowledge

Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation

Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar

Practical evaluation design skills: metrics, dataset curation, and structured experimentation

Experience with event-driven architectures, APIs, and microservices

A clear communicator equally comfortable with engineering teams and senior stakeholders

Strong hiring and team-building instincts with proven mentoring experience

English: Advanced (required for effective communication with global teams and client leadership).

7+ years of hands-on AI/ML engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility.

Databricks MLOps platform

LLM fine-tuning experience

Building agentic GenAI systems

Infrastructure as Code

Security and observability for AI services

Classical ML background

Open-source contributions