AI Engineering Manager
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
- 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
What about languages?
- English: Advanced (required for effective communication with global teams and client leadership).
How much experience must I have?
7+ 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 additional certifications tailored to your role.
- Access to Udemy Business, offering thousands of courses to boost