Job highlights

- 12+ years in software/AI engineering with expertise in Generative AI, LLMs, Agentic AI, Model Context Protocol, and cloud AI platforms (Azure, GCP)

- Lead design and implementation of enterprise-scale AI solutions, develop autonomous workflow platforms, architect multi-agent systems, drive AI-powered automation, establish AI governance and LLMOps frameworks, mentor engineering teams, and collaborate with stakeholders

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 skilled 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

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