Lead ML & Data Engineer - AI/ML Programs

Level: Senior / Lead

Experience: 9 to 12 years

Location: Magarpatta, Pune – Hybrid (2-3 days in office)

Working Hours: 12 pm to 9 pm IST (Mon to Fri)

About Simplify Healthcare:

Simplify Healthcare, a Simplify Group company, is a leading healthcare technology company focused on transforming how U.S. health plans (Payers) operate across benefits, provider, and claims functions. We build cloud-based, AI-driven SaaS platforms that modernize core payer workflows and enable scalable, compliant, and efficient operations.

Our flagship platform, Simplify Health Cloud™, brings together enterprise-grade solutions for product configuration, benefits administration, provider lifecycle management, claims operations, and experience orchestration. Designed specifically for the U.S. health insurance ecosystem, our platforms are built to support real-world regulatory complexity, operational scale, and continuous change.

Headquartered in Chicago, with a Global Development & Delivery Center in Pune and a global workforce of 900+ professionals, Simplify Healthcare supports more than 70 U.S. health plans. We are a bootstrapped, debt-free organization with over 18 years of profitable growth, combining long-term stability with a strong execution culture.

Our work has been consistently recognized by Deloitte (Technology Fast 500™), Inc. (Inc. 5000), FORTUNE, IDC, and Gartner. As we enter our next phase of growth, we continue to invest in applied AI, platform modernization, and leadership talent to shape the future of payer technology.

- Data Engineer experience remains a must-have along with practical, hands-on AI/ML experience. - this has changed as per last call

Role Summary:

- We are looking for a seasoned Lead ML/Data Engineer to architect and lead the design of scalable, governed,



and high-performance ML and data platforms, and to lead end-to-end ML programs spanning traditional ML, Data Science, Generative AI, and agentic AI use cases. This role drives technical direction across multi-cloud environments, owns the ML lifecycle from problem framing through model development, deployment, and monitoring, mentors engineering and data science teams, and ensures data quality, governance, and architectural excellence across the organization.

Key Responsibilities

- Lead the architecture, design, and delivery of enterprise-scale ML and data platforms across multi-cloud environments.
- Lead ML programs end-to-end, spanning traditional ML, Data Science, Generative AI, and agentic AI initiatives, from use case scoping and model design through experimentation, deployment, and monitoring.
- Guide the design and delivery of GenAI and agentic AI solutions, including retrieval-augmented generation (RAG), LLM orchestration, and multi-agent workflows, in partnership with product and engineering teams.
- Set standards for model evaluation, experimentation, MLOps, and responsible AI practices across the ML and Data Science function.
- Define and enforce ETL/ELT design patterns, dimensional modeling standards, and event-driven architecture practices.
- Establish data governance, access control, and data quality frameworks across teams.
- Drive performance tuning, cost optimization, and technical decision-making for large-scale data systems.
- Mentor engineers,



conduct design and code reviews, and set engineering best practices.
- Collaborate with business, analytics, and ML stakeholders to align platform roadmap with strategic goals.

Required & Preferred Skills

Must Have:

- AWS / Azure / GCP (at least 2 clouds)
- Design and develop production-ready AI/ML solutions, with strong grounding in core traditional ML (regression, classification, ensemble methods, time-series) and Data Science practice (statistical modeling, experimentation, hypothesis testing, feature engineering)
- Hands-on experience building Generative AI and agentic AI solutions, including LLM-based applications, RAG pipelines, prompt engineering, and agent orchestration frameworks (e.g., LangChain, LangGraph, Semantic Kernel, AWS agentic core)
- Proven track record leading ML programs and mentoring ML/DS engineers, including model lifecycle management, MLOps practices, and model evaluation/monitoring in production
- Databricks experience to design and develop ML and Data pipelines
- Data processing experience on distributed frameworks like Apache Spark
- ETL / ELT design patterns
- Event-driven architecture design with kafka/Eventhub/Kinesis
- Data governance & access control
- Data quality frameworks (Great Expectations, dbt tests)
- ML model performance Tuning
- Proficient in programming language Python / Scala
- File and Table formats (Parquet, ORC, Avro, Delta, Iceberg, Hudi)
- include ML , genAI, agentic, DS and traditional ML core skill sets in addition to DE. Cadidate should be able to lead ML programs.
- Experience with foundation model APIs and fine-tuning (OpenAI, Anthropic, Azure OpenAI, open-weight LLMs)
- Feature stores (Feast, Tecton)
- DevOps/MLOps
- Data modeling (SQL/NOSQL/DWH) – Valuable to have
- Advanced SQL – Good to have

📌 Machine Learning Engineer (Pune)
🏢 Simplify Healthcare
📍 Pune