Principal AI/ML Engineer

Optum; Bengaluru; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

Optum in Bengaluru, India, seeks a Senior AI Architect to lead enterprise-scale AI initiatives and architect multi-agent systems. You will drive design and deployment of Generative AI solutions leveraging LLMs, RAG, and prompt optimization, while aligning with MCP patterns and governance across Azure OpenAI and GCP.

The role requires deep expertise in AI platform engineering, distributed systems, microservices, Kubernetes, and strong Python proficiency.

Qualifications

• 12+ years of software engineering/AI/ML experience with focus on AI/Generative AI.
• Experience delivering enterprise-scale AI/ML platforms and automation.
• Hands-on with AI agents, multi-agent systems, and enterprise automation.
• Experience with MCP, A2A integrations, tool-calling frameworks.
• Experience with Azure OpenAI, OpenAI APIs, and cloud services (Azure, GCP).
• Strong Python skills and familiarity with LangChain/LangGraph/HF ecosystems.

Responsibilities

• Serve as technical leader for major commercial AI initiatives and enterprise-scale solutions.
• Design and deploy Generative AI solutions using LLMs, RAG, prompts, and embeddings.
• Architect multi-agent systems with A2A patterns and MCP standards.
• Drive AI-powered automation across apps, APIs, and legacy systems.
• Establish LLMOps, MLOps, governance, testing, and observability pipelines.
• Mentor AI/ML engineers and conduct architecture reviews and workshops.

Skills

Technical leadership

AI architecture

Gen AI platforms

LLMs & RAG

Multi-agent systems

MCP & A2A

Azure OpenAI

OpenAI APIs

LangChain

Python

Cloud platforms

Communication skills

Education

Bachelor's or Master's in CS/AI/ML

Tools

LangChain

LangGraph

OpenAI SDKs

Hugging Face

Kubernetes

Job description

Primary Responsibilities

• Serve as the technical leader for major commercial initiatives, driving the architecture, design, and implementation of enterprise-scale AI solutions that transform business processes, operations, and customer experiences
• Lead the development of Agentic AI and autonomous workflow platforms that automate complex business processes, orchestrate enterprise systems, and enable intelligent decision-making at scale
• Design and implement advanced Generative AI solutions leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt orchestration, vector databases, knowledge graphs, and AI agents
• Architect and build enterprise-grade multi-agent systems utilizing Agent-to-Agent (A2A) communication patterns, Model Context Protocol (MCP) standards, tool integration frameworks, and intelligent workflow orchestration capabilities
• Drive AI-powered workflow automation initiatives by integrating AI agents with business applications, APIs, legacy systems, databases, and enterprise platforms to modernize critical operational processes
• Establish best practices for prompt engineering, prompt optimization, evaluation frameworks, grounding strategies, context management, and response quality assurance across AI applications
• Lead the fine-tuning, adaptation, optimization, and deployment of foundation models and domain-specific LLMs to improve business relevance, response quality, and operational effectiveness
• Design and implement scalable AI/ML platforms supporting model training, inference, evaluation, observability, monitoring, governance, security, and lifecycle management
• Collaborate with product managers, business stakeholders, architects, and engineering teams to identify modernization opportunities and translate business requirements into AI-driven solutions with measurable outcomes
• Provide technical leadership and mentorship to AI/ML engineers, software engineers, and architects by conducting architecture reviews, code reviews, design workshops, and technical coaching
• Establish LLMOps, MLOps, and AI governance frameworks covering model lifecycle management, automated testing, evaluation pipelines, deployment automation, performance monitoring, and continuous improvement
• Drive adoption of AI engineering standards for responsible AI, model safety, security, explainability, auditability, privacy protection, and regulatory compliance
• Architect hybrid-cloud AI solutions across Azure and Google Cloud Platform (GCP) environments, enabling scalable and cost-effective deployment of enterprise AI services
• Evaluate emerging AI technologies, frameworks, foundation models, and industry trends, providing technical recommendations that accelerate modernization and innovation initiatives
• Partner with enterprise architecture, cybersecurity, infrastructure, and platform teams to establish reusable AI assets, engineering patterns, and reference architectures across the organization
• Broad business approach
• Resource to senior leadership
• Develops pioneering approaches to emerging industry trends
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related technical discipline
• 12+ years of professional software engineering, platform engineering, or AI/ML engineering experience with at least 5+ years focused on Artificial Intelligence, Machine Learning, and Generative AI solutions
• Proven experience designing and delivering enterprise-scale AI/ML platforms, intelligent automation solutions, and mission-critical software systems in production environments
• Hands-on experience building and deploying AI agents, multi-agent systems, autonomous workflows, and enterprise automation frameworks
• Solid experience implementing Model Context Protocol (MCP), Agent-to-Agent (A2A) integrations, tool-calling frameworks, and enterprise AI orchestration patterns
• Experience with Azure OpenAI, OpenAI APIs, and cloud-native AI services across Azure and Google Cloud Platform (GCP)
• Experience with LLMOps, MLOps, CI/CD pipelines, model monitoring, observability, experimentation frameworks, and production AI governance
• Deep expertise in Generative AI technologies, including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt engineering, prompt optimization, embeddings, vector databases, and foundation model architectures
• Demonstrated expertise in LLM fine-tuning, model adaptation techniques, evaluation frameworks, reinforcement learning concepts, and optimization strategies
• Solid understanding of AI platform engineering, distributed systems, microservices architecture, containerization, Kubernetes, API design, and event-driven architectures
• Proven ability to influence technical strategy, lead architecture decisions, and mentor engineering teams without direct managerial responsibility
• Proven solid programming skills in Python and experience with modern AI frameworks such as LangChain, LangGraph, OpenAI SDKs, Hugging Face, and related ecosystems
• Proven excellent communication, stakeholder management, and cross-functional collaboration skills with the ability to communicate complex AI concepts to both technical and business audiences

Preferred Qualifications

• Relevant certifications in Azure, Google Cloud, Machine Learning, Data Engineering, Generative AI, or Cloud Architecture
• Experience leading large-scale modernization or digital transformation programs involving AI-driven process automation and intelligent workflow orchestration
• Experience within healthcare, insurance, payer, financial services, or other highly regulated industries
• Experience implementing enterprise AI observability platforms, model evaluations, guardrails, hallucination detection, and quality monitoring solutions
• Experience developing reusable AI platforms, agent marketplaces, AI accelerators, or enterprise AI frameworks used across multiple business units
• Knowledge of Responsible AI, AI governance, security controls, privacy requirements, model risk management, and compliance frameworks
• Familiarity with knowledge graphs, semantic search, enterprise search platforms, graph databases, and advanced reasoning systems