Senior Consultant, AI Business Solutions

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN +1 · Technology Consulting

This role focuses on co-engineering Generative AI and agentic solutions for enterprise customers on Microsoft platforms. The consultant will translate ambiguous business needs into requirements, design and implement GenAI/agentic solutions, prototype rapidly, and ensure production-ready implementations with testing, monitoring, and secure deployment. Key responsibilities include identifying and mitigating AI-specific risks, building agentic workflows with tool invocation and guardrails, and implementing evaluation strategies for AI output quality and safety.

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 to 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

  • GenAI solutions
  • agentic solutions
  • Microsoft platforms (Dynamics 365, M365 Copilots, Power Platform, Azure)
  • integrations
  • APIs
  • automations
  • prototyping
  • production-ready implementations
  • testing
  • monitoring
  • secure deployment patterns
  • architecture
  • implementation
  • build/test specifications
  • eval and quality gates for GenAI outputs
  • operational guides/runbooks
  • risk mitigation (data access, prompt injection, hallucinations, privacy, compliance, latency, cost)
  • safeguards and fallback behaviors
  • deployment and live stabilization
  • troubleshooting integrations and model behavior
  • platform configuration
  • extensibility
  • knowledge transfer
  • documentation
  • reusable patterns
  • delivery plans (discovery, build, eval, deployment, operations)
  • dependencies across data, security, and enterprise systems
  • agentic workflows that invoke tools/APIs safely (function calling patterns)
  • guardrails
  • validation
  • auditability
  • evaluation strategies (offline/online)
  • output quality
  • groundedness
  • safety
  • business impact

Nice to have

  • consulting
  • customer stakeholders
  • business workflows
  • constraints
  • success metrics
  • ambiguous needs
  • solution hypotheses
  • sprint deliverables
  • POCs
  • users
  • enterprise systems

What the JD emphasized

  • implement agentic and GenAI solutions
  • agentic workflows
  • tool-use
  • orchestration
  • security
  • compliance
  • operational readiness
  • responsible AI
  • eval and quality gates for GenAI outputs
  • reliability
  • safety
  • maintainability
  • prompt injection
  • hallucinations
  • privacy
  • compliance
  • latency
  • cost
  • guardrails
  • validation
  • auditability
  • evaluation strategies

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