Lead AI/ML Engineer - Hybrid ML Optimisation ,Gurobi, CPLEX
Optum (UnitedHealth Group) · Healthcare & Pharma
- Bangalore, Karnataka, India
- Hybrid
- Posted yesterday
- Data & AI
About the job
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
We are seeking a highly experienced Lead AI/ML Engineer to lead the discovery, design, and adoption of advanced optimization and AI/ML solutions across mathematical programming, quantum-inspired methods, hybrid ML + optimization, and Generative AI domains.
This role serves as a senior technical leader responsible for driving optimization innovation, solving complex healthcare business problems, defining scalable solution strategies, and accelerating the transition of optimization solutions from experimentation to production.
Owns optimization 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:
Optimization Strategy and Technical Leadership - Drive optimization and AI solution strategy for complex, high-impact healthcare business problems
Lead technical design, solution architecture, and optimization technology selection decisions
Establish reusable optimization patterns, solver frameworks, standards, and best practices across the organization
Provide technical leadership and mentorship to AI/ML Engineers and cross-functional teams
Evaluate emerging optimization , quantum, and AI technologies and recommend enterprise adoption approaches
Influence enterprise AI and optimization strategy, architecture standards, and capability development
Optimization and AI/ML Modelling - Define the modelling strategy for complex business problems, setting the approach for mathematical optimization and AI/ML solution design across the enterprise
Set strategic direction for advanced AI/ML and optimization model development across predictive, prescriptive, deep learning, and GenAI systems
Own the formulation strategy for complex optimization problems, including linear and non-linear programming, integer and combinatorial optimization , and stochastic and robust optimization
Lead the development of new modelling paradigms combining ML and optimization , including decision-focused learning, reinforcement learning, and constrained optimization
Drive the enterprise strategy for GenAI and optimization integration, including retrieval optimization , prompt optimization , and constrained generation frameworks
Architect scalable modelling frameworks that integrate optimization solvers with ML/AI systems for enterprise-wide deployment
Champion quantum and quantum-inspired optimization methods, including QAOA, annealing approaches, and hybrid quantum-classical algorithms
Applied Solution Development - Design and develop POCs, prototypes, and reference implementations for optimization-driven use cases
Build reusable assets including solver configurations, optimization workflows, evaluation frameworks, and implementation accelerators
Define production-ready solution blueprints to support engineering adoption of optimization solutions
Lead end-to-end lifecycle activities including problem formulation, modelling, solver selection, validation, deployment, monitoring, and continuous improvement
Production Readiness and MLOps - Drive successful transition of validated optimization 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, optimization pipelines, solver integration patterns, and technical documentation
Research and Innovation - Define the research agenda in optimization , operations research, and quantum computing, directing investigation into high-impact healthcare applications
Lead evaluation and enterprise adoption decisions for emerging AI and optimization frameworks and technology stacks
Lead and sponsor publication of research artefacts including white papers, patents, and internal frameworks
Drive adoption of optimization accelerators, reusable frameworks, and best practices across teams
Responsible AI and Compliance - Establish evaluation, guardrail, and governance frameworks for optimization and AI solutions
Ensure explain ability of optimization 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
Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle
Team and Organizational Impact - Lead a small team of AI/ML Engineers while remaining deeply hands-on in optimization and AI solution development
Mentor team members on optimization methodologies, mathematical modelling, experimentation practices, and technical excellence
Promote knowledge sharing, innovation, and adoption of reusable optimization 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 and Leadership - Accelerate organizational adoption of optimization and AI by establishing repeatable patterns, reusable frameworks, and governance standards that reduce time-to-production
Influence enterprise AI and optimization 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 optimization architecture recommendations that balance business objectives, solver performance, scalability, and compliance requirements
Drive strategic alignment between optimization and AI solutions and business objectives across business, product, architecture, and engineering teams
Measuring Success - Quality and business impact of optimization , ML, and GenAI solutions
Production readiness and successful deployment of validated solutions
Percentage of POCs successfully adopted into production
Adoption of reusable optimization 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 and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
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 optimization -focused roles, with demonstrated leadership of enterprise-scale AI and optimization initiatives
Proven experience leading complex optimization and AI initiatives from ideation through production deployment
Hands-on experience with optimization 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 optimization -integrated generation frameworks
Experience developing Agentic AI solutions using orchestration frameworks and tool-enabled workflows
Demonstrated experience with combinatorial optimization and large-scale decision systems
Proven experience with hybrid ML and optimization approaches
Experience mentoring scientists and leading small technical teams
Solid foundation in mathematical optimization , operations research, and statistics
Solid expertise in machine learning, deep learning, statistical modelling, predictive analytics, and experimentation
Familiarity with quantum computing concepts or quantum-inspired algorithms for optimization
Solid knowledge of MLOps, model governance, and production AI and optimization systems
Proven solid programming skills in Python, NumPy, pandas, scientific computing, and SQL
Proven solid communication, stakeholder management, and technical leadership skills
Preferred Qualifications:
PhD or advanced degree in AI, ML, Computer Science, Mathematics, Statistics, Operations Research, or related discipline
Experience with quantum frameworks such as Qiskit, Cirq, D-Wave, or Azure Quantum
Experience with reinforcement learning for decision optimization
Experience with simulation-based optimization and digital twins
Healthcare domain expertise including claims, EHR/HL7/FHIR, care pathways, resource planning, ICD/CPT coding, risk adjustment, quality measures, and de-identification
Experience with publications or patents in optimization, ML, or quantum computing; contributions to internal frameworks, accelerators, or enterprise AI innovation initiatives
Experience integrating optimization into production systems in collaboration with engineering teams
Experience establishing Responsible AI, governance, risk management, and compliance frameworks for constrained optimization and decision-making systems
Experience building enterprise-scale Generative AI solutions integrated with optimization pipelines
Experience with Big data platforms including Databricks, Snowflake, BigQuery, and Kafka; lakehouse patterns
Experience with MLOps stack including MLflow, Sagemaker, Azure ML, or Vertex AI; model monitoring, observability, automated retraining, and deployment automation
Experience designing enterprise AI platforms, optimization solver services, and reusable ML frameworks
Experience mentoring teams and driving AI and optimization capability development across optimization
Knowledge of security and compliance frameworks including SOC 2, HITRUST, and HIPAA
Technical Skills:
Optimization: Mathematical optimization , Linear/Non-linear Programming, Integer and Combinatorial optimization , Stochastic optimization , Robust optimization , Operations Research, Large-Scale Decision Systems
Optimization Frameworks: Pyomo, OR-Tools, Gurobi, CPLEX
Quantum and Quantum-Inspired: Quantum optimization , QAOA, Quantum Annealing, Hybrid Quantum-Classical Algorithms, Qiskit, Cirq, D-Wave, Azure Quantum
AI/ML and Analytics: Machine Learning, Deep Learning, Statistical Modelling, Predictive Analytics, Prescriptive Analytics, Experimental Design
Hybrid ML + Optimization : Decision-Focused Learning, Reinforcement Learning, Constrained optimization , Simulation-Based optimization , Digital Twins
Generative and Agentic AI: Generative AI, LLMs, RAG, Prompt Engineering, Retrieval optimization , Constrained Generation, Agentic AI, Agentic Workflows, Orchestration Frameworks, Tool Integration
Programming and Data Engineering: Python, SQL, Feature Engineering, Data Pipelines, Model Evaluation, Experimentation Frameworks
MLOps and Model Lifecycle: MLOps, MLflow, Kubeflow, CI/CD, Model Monitoring, Observability, Drift Detection, Model Registry, Deployment Automation
Data Platforms: Databricks, Snowflake, BigQuery, Kafka, Lakehouse Architectures
Responsible AI and Governance: Responsible AI, Explainability, Fairness Constraints, AI Governance, Risk Management, SOC 2, HITRUST, HIPAA
Healthcare Analytics: Healthcare Analytics, Claims, Clinical Data, EHR/HL7/FHIR, ICD/CPT, Risk Adjustment, Population Health, Care Management
Leadership and Strategy: Technical Leadership, AI Strategy, optimization Strategy, Innovation, Stakeholder Management, Mentoring, Team Leadership, Cross-Functional Collaboration
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.