ML Optimization Engineer

Required Qualifications:

• 10+ 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:

• 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