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.

This role serves as a senior technical leader responsible for driving optimisation innovation, solving complex healthcare business problems, defining scalable solution strategies, and accelerating the transition of optimisation solutions from experimentation to production.

Owns optimisation strategy, architecture decisions, enterprise standards, reusable frameworks, capability development, and leadership of small teams while remaining deeply hands-on in solving critical business challenges.

Primary Responsibilities :

Optimisation Strategy & Technical Leadership :

- 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.

Optimisation & AI/ML Modelling :

- 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.

Applied Solution Development :

- 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.

Production Readiness & MLOps :

- 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.

Research & Innovation :

- 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.

Responsible AI & Compliance :

- 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.

Team & Organisational Impact :

- 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.

- Promote knowledge sharing, innovation, and adoption of reusable optimisation and AI capabilities.

- Collaborate with business, product, architecture, and engineering teams to align solutions with measurable business outcomes.

- Communicate solution results, trade-offs, and business impact to technical and non-technical stakeholders.

Stakeholder Engagement & Leadership :

- Accelerate organisational adoption of optimisation and AI by establishing repeatable patterns, reusable frameworks, and governance standards that reduce time-to-production.

- Influence enterprise AI and optimisation strategy through thought leadership, stakeholder engagement, and cross-functional collaboration.

- Communicate research findings, solution performance, strategic trade-offs, and business impact clearly to executive and non-technical stakeholders.

- Lead and own technical solution design discussions, providing authoritative AI and optimisation architecture recommendations that balance business objectives, solver performance, scalability, and compliance requirements.

- Drive strategic alignment between optimisation and AI solutions and business objectives across business, product, architecture, and engineering teams.

Measuring Success :

- Quality and business impact of optimisation, ML, and GenAI solutions.

- Production readiness and successful deployment of validated solutions.

- Percentage of POCs successfully adopted into production.

- Adoption of reusable optimisation accelerators, frameworks, and reference architectures.

- Reduction in experimentation-to-production cycle time.

- Team capability growth and delivery of measurable business outcomes.

- Comply with the terms and conditions of the employment contract, company policies and procedures, and any directives which may arise due to evolving business requirements.

Required 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.

- Experience developing Agentic AI solutions using orchestration frameworks and tool-enabled workflows.

- 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.

- Experience mentoring scientists and leading small technical teams.

Preferred Qualifications :

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