Primary Responsibilities

AI Data Platform Leadership

- Lead end-to-end design, implementation, and operation of AI-ready enterprise data platforms across lakehouse, data lake, warehouse, streaming, and event-driven architectures.
- Define scalable data engineering patterns for AI workloads, including ingestion, curation, transformation, feature preparation, semantic access, and governed consumption.
- Build reusable pipelines, APIs, data products, embeddings pipelines, vector indexing, semantic retrieval, and RAG-ready data services.
- Ensure data quality, observability, metadata, lineage, privacy, security, and compliance controls across AI data assets.

AI/ML Engineering & Production Delivery

- Lead delivery of AI/ML, Deep Learning, GenAI, Copilot, RAG, and Agentic AI products from development through deployment, monitoring, and ongoing production support.
- Drive AIDLC practices across data readiness, experimentation, model development, deployment, evaluation, observability, feedback loops, and continuous improvement.
- Ensure implementation of MLOps, LLMOps, CI/CD, automated testing, release automation, model lifecycle management, and production AI operations.
- Partner with data scientists, applied scientists, ML engineers, platform teams, product, and business stakeholders to operationalize AI solutions at enterprise scale.

Platform Adoption & Success Measurement

- Own adoption of AI data platforms across AI/ML teams, product teams, engineering groups, and enterprise consumers.
- Define and track platform success metrics including usage, reuse, onboarding time, data product adoption, pipeline reliability, SLA adherence, cost efficiency, and AI delivery acceleration.
- Establish platform documentation, enablement, onboarding, support models, and developer experience practices.
- Drive continuous improvement using telemetry, operational metrics, user feedback, platform performance, and business impact measures.

RAG & Agentic AI Enablement

- Enable data foundations for LLM applications, enterprise copilots, semantic search, RAG pipelines, and agentic workflows.
- Lead implementation of embeddings pipelines, vector stores, semantic layers, context engineering, knowledge retrieval, and AI data-serving patterns.




- Ensure GenAI and Agentic AI solutions are reliable, secure, governed, cost-efficient, and aligned to enterprise architecture standards.
- Promote reusable RAG templates, prompt workflows, retrieval services, orchestration patterns, and platform accelerators.

MLOps, LLMOps & Platform Excellence

- Drive enterprise adoption of MLOps, LLMOps, and AgentOps practices.
- Establish standards for CI/CD automation, model lifecycle management, evaluation frameworks, monitoring, observability, deployment automation, and governance controls.
- Partner with platform teams to strengthen AI infrastructure and shared services.

Required Qualifications

- 15+ years of experience in data engineering, AI/ML engineering, software engineering, or platform engineering.
- 5+ years of experience leading engineering teams and large-scale enterprise technology delivery programs.
- Strong hands-on background designing and implementing enterprise data platforms for AI/ML, Deep Learning, GenAI, RAG, Copilot, and Agentic AI workloads.
- Experience building AI-ready data capabilities including batch/streaming pipelines, ETL/ELT, feature engineering, semantic layers, metadata, data quality, lineage, and governed data access.
- Proven experience delivering AI/ML products and platforms from development through production deployment, monitoring, maintenance, and scaling.
- Experience with MLOps, LLMOps, AIDLC, CI/CD, model lifecycle management, observability, and production AI operations.
- Strong programming and data engineering skills using Python, SQL, Spark, PySpark, and contemporary cloud data engineering frameworks.
- Experience with Azure, AWS, and/or GCP data and AI platforms, distributed processing, APIs, microservices, orchestration, and production engineering practices.
- Strong stakeholder management, delivery leadership, communication, and problem-solving skills.





Preferred Qualifications

- Experience leading enterprise AI engineering organizations or multiple AI delivery teams.
- Experience establishing AIDLC frameworks, operating models, and AI governance processes.
- Experience building AI platforms, developer platforms, model serving platforms, or shared AI services.
- Expertise with LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar frameworks.
- Experience implementing enterprise Agentic AI and intelligent automation capabilities.
- Experience with MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, Databricks, or equivalent AI platforms.
- Experience with vector databases, semantic retrieval, and enterprise knowledge systems.
- Strong experience with Kubernetes, Docker, Infrastructure-as-Code, and platform automation.
- Healthcare, financial services, insurance, or other regulated industry experience.
- Experience driving reusable frameworks, accelerators, and engineering productivity initiatives.
- Contributions to enterprise AI platforms, innovation programs, patents, publications, or open-source initiatives.

Technical Skills

- Data Engineering: Python, SQL, Spark, PySpark, ETL/ELT, data modeling, data quality, feature engineering, streaming pipelines
- AI Data Platforms: Lakehouse, data lake, data warehouse, semantic layers, feature stores, metadata, lineage, governed access, data products
- AI/ML Engineering: AIDLC, MLOps, LLMOps, model lifecycle, deployment automation, evaluation, monitoring, observability
- GenAI & Retrieval: LLMs, RAG, embeddings, vector databases, semantic search, knowledge graphs, prompt workflows, context engineering
- Cloud & Platforms: Azure, AWS, GCP, Databricks, Snowflake, BigQuery, Kafka, Airflow, dbt, Kubernetes, Docker
- Architecture & Engineering: APIs, microservices, distributed systems, CI/CD, testing, platform engineering, production operations
- Governance & Security: Data privacy, access control, compliance, Responsible AI, model governance, auditability, security controls
- Leadership: Engineering management, platform adoption, stakeholder management, roadmap execution, success metrics, operational excellence

📌 Lead AI/ML Engineer (Bengaluru)
🏢 Optum
📍 Bengaluru