Overview

We are seeking a highly experienced Lead AI/ML Engineer to lead the discovery, design, and adoption of advanced optimisation and AI/ML solutions across mathematical programming, quantum-inspired methods, hybrid ML + optimisation, and Generative AI domains.

Responsibilities

• Drive optimisation and AI solution strategy for complex, high-impact healthcare business problems.
• Lead technical design, solution architecture, and optimisation technology selection decisions.
• Establish reusable optimisation patterns, solver frameworks, standards, and best practices across the organisation.
• Provide technical leadership and mentorship to AI/ML Engineers and cross-functional teams.
• Evaluate emerging optimisation, quantum, and AI technologies and recommend enterprise adoption approaches.
• Influence enterprise AI and optimisation strategy, architecture standards, and capability development.
• Define the modelling strategy for complex business problems, setting the approach for mathematical optimisation and AI/ML solution design across the enterprise.
• Set strategic direction for advanced AI/ML and optimisation model development across predictive, prescriptive, deep learning, and GenAI systems.
• Own the formulation strategy for complex optimisation problems, including linear and non-linear programming, integer and combinatorial optimisation, and stochastic and robust optimisation.
• Lead the development of new modelling paradigms combining ML and optimisation, including decision-focused learning, reinforcement learning, and constrained optimisation.
• Drive the enterprise strategy for GenAI and optimisation integration, including retrieval optimisation, prompt optimisation, and constrained generation frameworks.
• Architect scalable modelling frameworks that integrate optimisation solvers with ML/AI systems for enterprise-wide deployment.
• Champion quantum and quantum-inspired optimisation methods, including QAOA, annealing approaches, and hybrid quantum-classical algorithms.
• Design and develop POCs, prototypes, and reference implementations for optimisation-driven use cases.
• Build reusable assets including solver configurations, optimisation workflows, evaluation frameworks, and implementation accelerators.
• Define production-ready solution blueprints to support engineering adoption of optimisation solutions.
• Lead end-to-end lifecycle activities including problem formulation, modelling, solver selection, validation, deployment, monitoring, and continuous improvement.
• Drive successful transition of validated optimisation solutions into production by partnering with engineering teams to ensure scalability, maintainability, and security.
• Apply MLOps best practices including experiment tracking, solver versioning, CI/CD integration, performance monitoring, and observability.
• Ensure operational readiness, model governance, and alignment with enterprise architecture standards.
• Develop implementation-ready artefacts including reusable code, optimisation pipelines, solver integration patterns, and technical documentation.
• Define the research agenda in optimisation, operations research, and quantum computing, directing investigation into high-impact healthcare applications.
• Lead evaluation and enterprise adoption decisions for emerging AI and optimisation frameworks and technology stacks.
• Lead and sponsor publication of research artefacts including white papers, patents, and internal frameworks.
• Drive adoption of optimisation accelerators, reusable frameworks, and best practices across teams.
• Establish evaluation, guardrail, and governance frameworks for optimisation and AI solutions.
• Ensure explainability of optimisation decisions, fairness constraints, and regulatory alignment with HIPAA/PHI, SOC 2, and HITRUST.
• Apply responsible AI principles, bias mitigation, and AI governance frameworks throughout the solution lifecycle.
• Lead a small team of AI/ML Engineers while remaining deeply hands-on in optimisation and AI solution development.
• Mentor team members on optimisation methodologies, mathematical modelling, experimentation practices, and technical excellence.

Qualifications

• Bachelor's degree in Computer Science, Mathematics, Engineering, Operations Research, Applied Mathematics, or related field; Master's degree preferred.
• 15+ years of experience in applied AI/ML or optimisation-focused roles, with demonstrated leadership of enterprise-scale AI and optimisation initiatives.
• Proven experience leading complex optimisation and AI initiatives from ideation through production deployment.
• Strong foundation in mathematical optimisation, operations research, and statistics.
• Strong expertise in machine learning, deep learning, statistical modelling, predictive analytics, and experimentation.
• Hands-on experience with optimisation frameworks, including Pyomo, OR-Tools, Gurobi, and/or CPLEX.
• Hands-on experience with ML/DL frameworks: PyTorch and/or TensorFlow.
• Hands-on experience with Generative AI technologies including LLMs, RAG, prompt engineering, and optimisation-integrated generation frameworks.
• Demonstrated experience with combinatorial optimisation and large-scale decision systems.
• Proven experience with hybrid ML and optimisation approaches.
• Familiarity with quantum computing concepts or quantum-inspired algorithms for optimisation.
• Strong programming skills in Python, NumPy, pandas, scientific computing, and SQL.
• Strong knowledge of MLOps, model governance, and production AI and optimisation systems.
• Strong communication, stakeholder management, and technical leadership skills.

Nice to have

• PhD or advanced degree in AI, ML, Computer Science, Mathematics, Statistics, Operations Research, or related discipline.