Principal Consultant, AI Business Solutions

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN +2 · Technology Consulting

Principal Consultant for AI Business Solutions at Microsoft, focusing on co-engineering GenAI and agentic solutions for enterprise customers on Microsoft platforms. The role involves translating ambiguous business needs into working prototypes and production-ready solutions, with a strong emphasis on end-to-end delivery, risk mitigation, and operational readiness. Responsibilities include implementing agentic workflows, integrating with customer environments, and ensuring the security, compliance, and reliability of AI systems.

What you'd actually do

  1. Embed with customer stakeholders to understand business workflows, constraints, and success metrics; translate ambiguous needs into clear requirements, solution hypotheses, and sprint deliverables for GenAI/agentic scenarios.
  2. Design and implement agentic and GenAI solutions on Microsoft platforms (e.g., Dynamics 365, M365 Copilots, Power Platform, Azure) including integrations, APIs, and automations that accelerate adoption and deliver measurable outcomes.
  3. Prototype rapidly, then harden to production: build POCs for agentic workflows (multi-step tasks, tool-use, orchestration), validate with users, and evolve production-ready implementations with testing, monitoring, and secure deployment patterns.
  4. Own end-to-end delivery from architecture through implementation: define build/test specifications, create eval and quality gates for GenAI outputs, and produce operational guides/runbooks for reliability, safety, and maintainability.
  5. Identify and mitigate business/technical risks specific to AI systems (data access, prompt injection, hallucinations, privacy, compliance, latency, cost); propose safeguards and fallback behaviors for agentic flows.

Skills

Required

  • Bachelor’s degree in computer science, Engineering, or related field AND demonstrated experience delivering production software solutions (or equivalent experience).
  • 18+ years’ experience delivering enterprise-scale solutions on Microsoft platforms (Dynamics 365 / M365 Copilot/ Power Platform / Azure), including integration, security, and deployment in customer environments.

Nice to have

  • Proven experience delivering hands‑on, production-grade solutions across the full delivery lifecycle, including requirement ambiguity, development, deployment, and post‑go‑live stabilization.
  • Proven delivery of Dynamics 365 CE/Power Platform solutions, M365 Copilot Solutions, plus experience building AI-infused workflows (recommendations, summarization, case routing, contact center assist, sales/service copilots) with production-grade engineering practices.
  • Experience building or extending solutions involving Copilots, AI‑infused workflows, or intelligent automation is a strong plus.
  • Certifications and/or experience in Azure Solutions, Dynamics 365, M365 Copilot and Power Platform

What the JD emphasized

  • co-engineering GenAI and agentic solutions
  • rapid, iterative delivery cycles
  • responsible AI solutions
  • security, compliance, and operational readiness
  • agentic workflows (multi-step tasks, tool-use, orchestration)
  • eval and quality gates for GenAI outputs
  • business/technical risks specific to AI systems (data access, prompt injection, hallucinations, privacy, compliance, latency, cost)
  • agentic workflows that invoke tools/APIs safely (function calling patterns), with guardrails, validation, and auditability
  • evaluation strategies (offline/online) to measure output quality, groundedness, safety, and business impact

Other signals

  • co-engineering GenAI and agentic solutions
  • rapid, iterative delivery cycles
  • responsible AI solutions
  • security, compliance, and operational readiness
  • translate ambiguous requirements into working software
  • integrate with real customer environments
  • own outcomes through deployment and stabilization
  • implement agentic and GenAI solutions on Microsoft platforms
  • prototype rapidly, then harden to production
  • build POCs for agentic workflows (multi-step tasks, tool-use, orchestration)
  • validate with users, and evolve production-ready implementations
  • testing, monitoring, and secure deployment patterns
  • own end-to-end delivery from architecture through implementation
  • create eval and quality gates for GenAI outputs
  • produce operational guides/runbooks for reliability, safety, and maintainability
  • identify and mitigate business/technical risks specific to AI systems (data access, prompt injection, hallucinations, privacy, compliance, latency, cost)
  • propose safeguards and fallback behaviors for agentic flows
  • support deployment and live stabilization
  • troubleshoot integrations and model behavior in real environments
  • iterate quickly, and ensure successful go-live with clear ownership, monitoring, and incident playbooks
  • balance platform configuration with extensibility to maximize speed and maintainability
  • co-engineer with customer teams to ensure knowledge transfer, documentation, and reusable patterns
  • drive delivery plans across the full lifecycle—discovery, build, eval, deployment, and operations
  • accounting for dependencies across data, security, and enterprise systems
  • build agentic workflows that invoke tools/APIs safely (function calling patterns), with guardrails, validation, and auditability
  • implement evaluation strategies (offline/online) to measure output quality, groundedness, safety, and business impact
  • capture repeatable assets (templates, reference architectures, accelerators) and share feedback loops with internal teams