ML Optimization Engineer

Job location: Noida, Hyderabad, Bengaluru Overall experience: 10 to 18 years Employment type: Full Time, Permanent Job type: fulltime Salary: 35-75 Lacs 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