Lead AI Engineer

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About the role

Leadership and Delivery • Lead project delivery end to end, with clear governance, stakeholder communication, and accountability for outcomes • Build and mentor a high-performing AI engineering team, establishing technical standards and fostering a culture of quality and pragmatism • Own proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs clearly to clients • Define what AI systems should and should not attempt, setting realistic expectations and being upfront about limitations • Conduct technical reviews and architectural assessments to maintain high standards across projects and team AI Development • Guide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions that are robust enough for production • Lead the application of advanced prompt engineering techniques including instruction design, few-shot sets, structured outputs, and tool/agent prompts • Run feasibility assessments to choose the right approach for each problem: prompting, RAG, fine-tuning, or classical ML • Mentor engineers on end-to-end AI system design and production deployment practices Evaluation and Quality • Design evaluation frameworks including LLM-as-a-judge approaches, metric creation (recall@k, precision@k), and go/no-go gates • Lead structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence not intuition • Establish team practices for identifying and categorising model failures including hallucinations, retrieval misses, and instruction-following errors • Set quality standards that ensure AI systems meet production reliability requirements MLOps and Infrastructure • 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 What We Are Looking For • 5+ years building and deploying AI solutions in production environments • 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 What about languages? • English: Advanced (required for effective communication with global teams and client leadership). How much experience must I have? 5+ years of hands-on AI/ML engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility. Nice to Have • 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 Our Perks and Benefits: 📚 Learning Opportunities: • Certifications in AWS (we are AWS Partners), Databricks, and Snowflake • Access to AI learning paths to stay up to date with the latest technologies • Study plans, courses, and

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