Sr Manager Software Engineering

Overview

The Sr Engineering Manager is a transformational technology leader responsible for building high-performing engineering teams and driving enterprise-wide adoption of AI-native software engineering practices. The role combines people leadership, technical excellence, delivery accountability, architectural governance, and AI transformation leadership.

What you'll do

• Define and execute the AI engineering adoption strategy across software development teams.
• Establish AI-first engineering practices across requirements analysis, design, coding, testing, documentation, deployment, operations, and support.
• Drive adoption of approved AI coding assistants, engineering copilots, autonomous agents, and AI-enabled developer platforms.
• Identify opportunities to automate engineering, testing, documentation, operational, and support activities using AI.
• Lead organizational change from traditional software development models to AI-augmented engineering practices.
• Establish governance frameworks, standards, quality controls, validation processes, security reviews, intellectual property safeguards, playbooks, and adoption guidelines for AI-assisted development.
• Ensure adherence to enterprise AI governance, risk management, security, and compliance requirements.
• Lead, mentor, coach, and develop software engineers, technical leads, and senior engineering talent.
• Drive workforce planning, hiring, onboarding, succession planning, performance management, career development, and talent retention.
• Coach engineers on prompt engineering, AI-assisted design, AI-powered testing, AI-driven troubleshooting, and autonomous agents.
• Build internal AI champions and communities of practice.
• Provide technical leadership through architecture reviews, design reviews, code reviews, and technology evaluations.
• Guide teams in integrating Generative AI services, LLM platforms, RAG architectures, agentic workflows, MCP servers, and emerging AI technologies.
• Ensure engineering solutions meet standards for scalability, performance, reliability, maintainability, observability, and security.
• Own delivery of complex software initiatives across multiple teams.
• Manage technical risks, dependencies, stakeholder expectations, and cross-functional alignment.
• Lead production diagnostics, incident management, root cause analysis, reliability improvements, and technical debt reduction.
• Design and execute AI capability development programmes, workshops, training sessions, architecture forums, and innovation programmes.
• Sponsor PoCs, pilots, and experimentation initiatives for emerging AI technologies.
• Partner with platform, architecture, product, security, and engineering teams to evolve AI-enabled developer experiences and engineering tooling.
• Measure AI adoption, engineering productivity, quality, governance, automation coverage, team engagement, retention, career progression, and AI capability growth.

What you'll need

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline.
• 10+ years of software engineering experience delivering enterprise-scale software solutions.
• 5+ years of engineering leadership experience with direct people management responsibilities.
• Proven experience leading distributed Agile engineering teams.
• Demonstrated success delivering complex cloud-native and distributed systems.
• Hands-on experience with AI-assisted software development platforms such as GitHub Copilot, Microsoft Copilot, Cursor, Claude Code, or equivalent technologies.
• Strong understanding of Generative AI, LLMs, RAG architectures, agentic workflows, MCP servers, prompt engineering, and AI governance.
• Experience evaluating, implementing, and scaling AI technologies within engineering organisations.
• Ability to define engineering standards and governance models for AI-assisted development.
• Demonstrated capability to measure and deliver productivity improvements through AI adoption.
• Strong software engineering background in Java, Python, microservices, APIs, distributed systems, and cloud-native architectures.
• Experience with Google Cloud platforms.
• Deep understanding of CI/CD, automated testing, DevOps, observability, operational excellence, and modern software delivery practices.
• Strong architectural design, system thinking, and technical decision-making capabilities.

Details

• Location: Bengaluru, Karnataka, India.