Lead Ai Ml Engineering Manager

Optum; Bengaluru; Hybrid; INR 4,200,000 - 7,000,000; Full time

Job summary

Optum in Bengaluru seeks a hands-on technical leader to design, build, and productionize AI/ML and GenAI solutions that improve healthcare operations and patient outcomes. You will own end-to-end delivery—from problem framing to data pipelines, models, MLOps/LLMOps, and monitoring—while mentoring a small team and partnering with product, data engineering, and clinical stakeholders.

Responsibilities include leading architecture and delivery of ML/GenAI at production scale, building robust data

Qualifications

• Bachelor's-level degree in CS/Engineering/Math or related field; advanced degree preferred.
• 10+ years in software/ML engineering with leadership experience.
• Hands-on GenAI: LLMs, embeddings, RAG, fine-tuning; familiar with Hugging Face and LangChain/LlamaIndex.
• Strong data engineering skills: SQL; experience with Spark/Dask and Airflow/Prefect.
• Deep learning expertise (PyTorch/TensorFlow) and modern NLP (transformers).
• Expert Python and pandas stack; high-performance code; strong testing (pytest) and Git.
• Cloud proficiency (AWS/Azure/GCP) and containerization/orchestration (Docker/Kubernetes); CI/CD and IaC (Terraform).
• Strong communication and stakeholder management skills.

Responsibilities

• Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, DL, LLM apps) at production scale.
• Build robust data/feature pipelines over large clinical/claims datasets with Python/SQL.
• Develop and deploy LLM capabilities: prompts, fine-tuning, RAG, vector indexing, evaluation with guardrails.
• Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring.
• Ensure data privacy/compliance (HIPAA/PHI), governance and Responsible AI.
• Translate business problems into technical roadmaps; communicate trade-offs to executives.
• Mentor engineers and set engineering standards (code reviews, documentation, observability).
• Collaborate with product, data engineering, and clinical/operations teams to drive impact.

Skills

GenAI experience

LLMs

NLP transformers

Python

Pandas

SQL

Spark/Dask

Airflow/Prefect

Cloud: AWS/GCP/Azure

Docker/Kubernetes

CI/CD

Education

Bachelors in CS/Engineering/Math

MS/PhD preferred

Tools

Hugging Face

LangChain

LlamaIndex

Kubernetes

MLflow

SageMaker

Azure ML

Vertex AI

FAISS

Pinecone

Neo4j

Job description

Were looking for a hands-on technical leader to design, build, and productionize AI/ML and GenAI solutions that improve healthcare operations and patient outcomes. You will own end-to-end delivery-from problem framing and data pipelines to models, MLOps/LLMOps, and ongoing monitoring-while mentoring a small team and partnering with product, data engineering, and clinical/operations stakeholders.

Primary Responsibilities:

• Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, deep learning, LLM apps) at production scale
• Build robust data/feature pipelines over large clinical/claims datasets; write efficient, well-tested Python (pandas/NumPy) and SQL
• Develop and deploy LLM capabilities: prompt design, fine-tuning, RAG pipelines, vector indexing, and evaluation with guardrails
• Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring, drift detection, A/B testing
• Ensure data privacy and compliance (HIPAA/PHI handling, access controls, auditability) and champion model governance and Responsible AI
• Translate business problems into technical roadmaps; communicate trade-offs and results to executives and non-technical partners
• Mentor engineers and set engineering standards (code reviews, documentation, reliability, observability)
• Lead architecture and delivery of ML/GenAI solutions (classification, forecasting, NLP, deep learning, LLM apps) at production scale
• Build robust data/feature pipelines over large clinical/claims datasets; write efficient, well-tested Python (pandas/NumPy) and SQL
• Develop and deploy LLM capabilities: prompt design, fine-tuning, RAG pipelines, vector indexing, and evaluation with guardrails
• Establish MLOps/LLMOps best practices: CI/CD, model registry, experiment tracking, monitoring, drift detection, A/B testing.
• Ensure data privacy and compliance (HIPAA/PHI handling, access controls, auditability) and champion model governance and Responsible AI
• Translate business problems into technical roadmaps; communicate trade-offs and results to executives and non-technical partners
• Mentor engineers and set engineering standards (code reviews, documentation, reliability, observability)
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Qualifications -

Required Qualifications:

• Bachelors in Computer Science, Engineering, Math, or related field required; MS/PhD preferred or equivalent experience
• 10+ years of professional experience in software/ML engineering, including 3+ years leading projects or teams
• Hands-on GenAI experience: LLMs, embeddings, RAG, fine-tuning, evaluation; familiarity with Hugging Face and LangChain/LlamaIndex
• Solid data engineering skills: SQL; experience with Spark/Dask and workflow orchestration (Airflow/Prefect)
• Solid foundation in statistics and ML (hypothesis testing, experimental design, feature engineering, supervised/unsupervised methods)
• Deep learning expertise (PyTorch or TensorFlow) and modern NLP (transformers)
• Expert Python and pandas stack; ability to write vectorized, high-performance code; solid testing practices (pytest) and Git
• Cloud proficiency (AWS/Azure/GCP) and containerization/orchestration (Docker/Kubernetes); CI/CD and IaC (Terraform) exposure
• Proven excellent communication and stakeholder management skills

Preferred Qualifications:

• Healthcare domain experience: claims, EHR/HL7/FHIR, coding (ICD/CPT), risk adjustment, quality measures, de-identification
• Experience with Big data platforms (Databricks, Snowflake, BigQuery) and streaming (Kafka); lakehouse patterns
• Experience with MLOps stack: MLflow/SageMaker/Azure ML/Vertex; model monitoring/observability
• Vector databases (FAISS, Pinecone, pgvector), knowledge graphs (Neo4j), and ontologies (UMLS/SNOMED)
• Security/compliance frameworks (SOC 2, HITRUST) experience
• Experience with additional languages for performance or integration (Scala/Java/Go)