Copilot Studio Technical Consultant
Accenture · Bengaluru
Role Summary
- Own the design and delivery of agentic AI and Copilot Studio skilling programmes for large enterprise audiences — curriculum design, instructor-led sessions, hands-on labs, self-paced content, assessments and certification pathways.
- Run train-the-trainer and champion-enablement programmes so client teams can sustain and scale agent development independently after the engagement.
- Teach agent design as a discipline, not a tool walkthrough — task decomposition, orchestration and reasoning patterns, tool/action design, grounding strategy, memory and context, human-in-the-loop checkpoints, failure modes and cost/latency trade-offs.
- Tailor content and depth to distinct audiences — business makers, pro-code engineering teams, and business and IT leadership.
- Build reference and demo agents in Copilot Studio — topics, generative answers, actions and connectors, agent flows, knowledge sources and multi-agent handoffs — to anchor training and seed client use cases.
- Design and maintain lab environments, sandbox tenants and exercise sets, keeping them current with the fast-moving agent-platform roadmap.
- Provide hands-on advisory on live engagements: agent design reviews, grounding and knowledge-source guidance, action/connector patterns, prompt and instruction design, testing and evaluation approaches, and go-live readiness.
- Advise on when to build in Copilot Studio versus a pro-code approach on Azure AI Foundry or an agent framework — and how the two coexist in a single client landscape.
- Establish evaluation and Responsible AI practice for agents: test sets and regression suites, groundedness and quality measurement, content safety, guardrails, human oversight and monitoring in production.
- Advise clients on agent governance and operating models — environment strategy, DLP policies, maker onboarding, publishing and approval workflows, agent lifecycle management, licensing considerations and usage analytics.
- Support use-case discovery workshops — identifying, qualifying and prioritising agent scenarios, and setting realistic expectations on what agents can and cannot do reliably.
- Act as the practice reference point for agentic AI enablement: build reusable training assets, playbooks, agent templates and accelerators, and mentor consultants joining the capability.
- Contribute to presales as the subject-matter expert — enablement approach, effort and duration estimates, demos and client-facing solution walkthroughs.
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Required Skills & Experience
- 4–12 years of overall experience in software engineering, automation, data or AI roles, with a recent and substantial focus on generative and agentic AI.
- Hands-on experience designing and building agentic AI solutions — multi-step orchestration, tool and function calling, retrieval and grounding, memory and context management, and multi-agent collaboration patterns.
- Up to 2 years of hands-on experience with Microsoft Copilot Studio, covering both building agents and delivering training on the platform.
- Practical Copilot Studio depth: topics and trigger design, generative answers and knowledge sources, actions and connectors, agent flows, publishing across channels (Teams, web, M365 Copilot), and testing and troubleshooting agents.
- Demonstrated experience designing and delivering technical training to large organisations — instructor-led workshops, hands-on labs, train-the-trainer and champion programmes, for both business and technical audiences.
- Working knowledge of the wider Microsoft AI stack: Azure AI Foundry / Azure OpenAI Service, Azure AI Search for retrieval, and how these complement low-code agents.
- Familiarity with agent frameworks and protocols — Semantic Kernel, AutoGen, LangChain/LangGraph, MCP or similar — sufficient to position and compare approaches credibly.
- Understanding of prompt and instruction design, RAG patterns, and agent evaluation — groundedness, task success, regression testing and quality measurement.
- Understanding of Responsible AI in practice: content safety and filtering, guardrails, data boundaries, auditability and human oversight.
- Working knowledge of the Power Platform (Power Automate, Dataverse) and of Microsoft 365 data sources as grounding and action surfaces for agents.
- Comfort with code — Python or C#, plus REST APIs and integration patterns — enough to build custom actions, explain pro-code extensions and debug integrations.
- Strong facilitation and communication skills, with the confidence to hold a room of senior stakeholders as well as a lab of hands-on makers.
- Ability to produce enablement collateral independently — decks, lab guides, quick-reference material and recorded walkthroughs.
Preferred / Good to Have
- Microsoft certifications: Azure AI Engineer Associate (AI-102), Power Platform Fundamentals (PL-900) or Developer (PL-400), or Copilot/agent-related Microsoft credentials.
- Experience with declarative agents, the Microsoft 365 Agents Toolkit, Graph connectors or custom engine agents for deeper extensibility scenarios.
- Experience taking agents to production — observability, cost and latency optimisation, versioning and lifecycle management.
- Exposure to non-Microsoft agent platforms (AWS Bedrock Agents, Google Vertex AI Agents, open-source frameworks) for comparative positioning.
- Experience across multiple industry domains (manufacturing, BFSI, consumer goods, energy, healthcare, or others).
- Experience in a global delivery / SI environment (Avanade, Accenture, or similar) working with distributed teams across geographies.
- Prior contribution to reusable training assets, agent accelerators or practice-building initiatives.
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