Principal Software Engineering Manager

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN · Software Engineering

Principal Group Software Engineering Manager to lead a multi-team engineering organization transforming the sales resource planning space with AI-native, agentic systems. The role involves defining technical direction, guiding architecture, building and growing a high-performing organization, and partnering across functions to ship a composable, AI-native platform.

What you'd actually do

  1. Lead and nurture a multi-team engineering organization. Set clear performance expectations, manage performance, and build strong succession pipelines. Foster an inclusive and trust culture, grow talent, and establish the operating rhythms that keep teams aligned, accountable, and shipping.
  2. Define the modular core primitives and the contract-driven, composable architecture that lets teams build independently at the edge while the core stays coherent, and drive architectural decisions that balance velocity, reliability, privacy, and extensibility.
  3. Cultivate an AI-native engineering culture — both in what you build (agentic, intelligent planning experiences) and how you build it (AI-accelerated design, development, testing, and operations). Stay ahead of AI tooling trends, raise the bar on AI fluency across your teams, and make AI leverage a durable, data-backed source of productivity in how the organization works.
  4. Engineering fundamentals: design quality, code health, security, privacy, performance, reliability, and cost/COGS discipline. Own service reliability and live-site health, the timeliness and accuracy of planning cycles, and end-to-end ownership from design through operations — investing in the engineering systems, CI/CD, observability, and incident management that make quality and velocity compounding.
  5. Turn strategy into a predictable delivery cadence across teams. Balance long-term platform investment with near-term commitments; manage dependencies, risk, and trade-offs; and ship durable outcomes on time — especially through the demanding rhythm of annual planning transitions.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 18+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Nice to have

  • 6+ years of people management experience leading software engineering teams, preferably experience managing managers, with a track record of leading and growing teams.
  • Experience leading teams that span, partner, and vendor resources toward shared outcomes.
  • Technical and engineering leadership with the experience to define architecture, drive execution strategy, and deliver large-scale distributed platforms and services with measurable business impact.
  • Experience building relationships with and driving consensus among senior executives and cross-functional stakeholders in large or strategic organizations, and of managing stakeholder relationships to reach agreement on solutions.
  • Experience driving decisions collaboratively, resolve conflicts, and ensure follow-through, with written and verbal communication and presentation skills across all levels of management and audience sizes.
  • Experience building AI-native, agentic products and/or applying AI across the software development lifecycle with measurable productivity gains.
  • Experience building composable, contract-driven platforms at scale, with deep expertise in distributed systems, cloud platforms (e.g., Azure), and data-intensive applications.
  • Experience in commercial systems, including incentives, sales planning, CRM, and/or marketplaces.
  • Experience scaling multi-team engineering organizations, e.g., as a Group Engineering Manager or Director of Engineering.

What the JD emphasized

  • AI-native
  • agentic
  • AI-accelerated
  • AI fluency
  • AI leverage
  • AI-native, agentic products
  • applying AI across the software development lifecycle

Other signals

  • AI-native engineering culture
  • agentic, intelligent planning experiences
  • AI-accelerated design, development, testing, and operations
  • AI leverage a durable, data-backed source of productivity