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

📌 ML Optimization Engineer (Noida)
🏢 Optum
📍 Noida