Principal Engineer – GenAI in SDLC (Java & Agentic AI)

Overview

Principal Engineer to lead the enterprise strategy, architecture and implementation of Generative AI, AI Agents, AI Teammates and Intelligent Automation across the Software Development Lifecycle, enabling developers to leverage AI safely, effectively and at scale.

What you'll do

• Act as the enterprise technical authority for AI-Augmented SDLC, Agentic Engineering, and AI-Native Software Development practices.
• Define the technical vision, architecture, and roadmap for integrating Generative AI capabilities across the software development lifecycle.
• Lead enterprise-wide adoption of AI Agents, AI Teammates, Developer Assistants, and Intelligent Automation platforms.
• Establish reusable frameworks, standards, governance models, and reference architectures for AI-enabled engineering solutions.
• Design and implement autonomous and semi-autonomous AI Agents supporting software planning, development, testing, code review, vulnerability remediation, release automation, documentation generation, and knowledge discovery.
• Define Multi-Agent and Human-in-the-Loop architectures that balance automation, engineering productivity, and operational risk management.
• Build enterprise integrations leveraging Model Context Protocol (MCP), Knowledge Foundation platforms, retrieval systems, and developer productivity tooling.
• Drive the integration of Generative AI capabilities across Internal Developer Portals, CI/CD platforms, IDEs, developer workflows, and release pipelines.
• Establish self-service AI engineering capabilities that enable development teams to accelerate software delivery while improving quality and consistency.
• Develop enterprise observability, telemetry, adoption, and value realization strategies for AI-enabled engineering solutions.
• Partner with Security, Risk, Compliance, Architecture, and Technology leadership teams to establish responsible AI governance and controls.
• Influence engineering strategy across multiple technology organizations and serve as a trusted advisor to senior technology executives.
• Mentor Principal Engineers, Distinguished Engineers, Architects, and Engineering leaders while establishing enterprise standards for AI-Native Software Engineering.

What you'll need

• 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
• Strong Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
• 5+ years of experience architecting and building enterprise-scale distributed systems and cloud-native platforms.
• Extensive experience designing and scaling developer platforms, software engineering ecosystems, and enterprise engineering capabilities.
• Demonstrated success defining technology strategy and influencing architecture decisions across large, complex organizations.
• Strong expertise in Java-based engineering, microservices architecture, cloud-native application development, and modern software engineering practices.
• Experience implementing enterprise DevOps, CI/CD, platform engineering, and developer productivity solutions.
• Experience evaluating, implementing, or scaling AI-enabled engineering capabilities, developer assistants, or intelligent automation platforms.
• Strong leadership, stakeholder management, and enterprise architecture communication skills.

Nice to have

• Experience leading enterprise AI transformation, AI-Augmented SDLC, or Agentic Engineering initiatives.
• Deep understanding of Generative AI platforms, Large Language Models (LLMs), and AI application architectures.
• Experience with AI coding platforms including GitHub Copilot Enterprise, Devin AI, Claude Code, Cursor, or similar AI-assisted development tools.
• Expertise with Agentic AI frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, or OpenAI Agents Framework.
• Experience building Multi-Agent Systems, Human-in-the-Loop architectures, AI Teammates, and autonomous workflow orchestration solutions.
• Knowledge of Model Context Protocol (MCP), enterprise tool integration, context engineering, and developer workflow automation.
• Experience implementing Retrieval Augmented Generation (RAG), enterprise search, semantic search, and knowledge foundation capabilities.
• Experience with vector databases and AI retrieval platforms including Azure AI Search, Pinecone, Weaviate, Milvus, or similar technologies.
• Deep understanding of AI governance, AI observability, model evaluation, prompt engineering, agent evaluation, and responsible AI practices.
• Financial services or highly regulated industry experience.
• Strong thought leadership demonstrated through enterprise transformation initiatives, technical publications, conference presentations, or industry contributions.

Details

• Location: Bengaluru, India.
• Hybrid work model; occasional travel may be required based on business needs.
• Posting end date: 27 Oct 2026.