Leading the design and delivery of AI‑enabled enterprise platforms, from early architecture through to production
Building and improving applied AI solutions (including LLM-based patterns such as embeddings, retrieval approaches, and model tuning where appropriate)
Setting engineering standards for MLOps: CI/CD, model deployment, monitoring, evaluation, and operational reliability
Architecting secure, cloud-native services (microservices, APIs, messaging/event-driven patterns) that support AI workloads at scale
Coaching engineers and collaborating across Engineering, Data Science, and Product to turn complex AI concepts into practical delivery plans

Experience building, deploying, and maintaining machine learning and generative AI solutions (e.g., LLMs, embeddings, retrieval patterns, vector search, model fine-tuning)
Strong MLOps knowledge across continuous integration, deployment, monitoring, and evaluation of AI/ML models
Experience with AI infrastructure (orchestration pipelines, GPU compute, model hosting services) and scalable AI data structures (including vector databases/semantic search)
Deep software engineering capability in .NET Core/C# and modern back-end engineering, with flexibility to work across languages such as Python, Go, or TypeScript
Strong understanding of distributed systems, microservices, cloud-native patterns, and modern data stores (SQL and NoSQL)
Secure coding mindset with awareness of cybersecurity risks, particularly around AI and data privacy
Proven technical leadership (principal engineer/tech lead/architect level), with a track record of mentoring and influencing engineering direction

Principal Engineering Lead – AI Enabled Solutions

Key skills