Job Description:

Key Responsibilities:

- Run inception workshops with business teams.

- Document current-state process maps.

- Identify high-value agentic AI use cases.

- Define success metrics, ROI assumptions, and adoption goals.

- Help business teams redesign workflows around human-in-the-loop AI.

- Support pilot users, collect feedback, and drive adoption.

- Build prompt literacy and agent literacy across business teams.

- Identify role-level AI skills gaps as part of the paired engineering phase.

- Co-design AI agents with business and engineering teams to automate or augment workflows.

- Translate business processes into agent workflows, including task decomposition, decision logic, and tool usage.

- Define agent roles, boundaries, escalation paths, and human-in-the-loop controls.

- Collaborate with engineering teams on agent requirements, data needs, APIs, and integrations.

- Develop and iterate prompts, instructions, and evaluation criteria for AI agents.

- Establish guardrails for responsible AI use, compliance, and risk mitigation in agent design.

- Test, validate, and monitor AI agent performance against defined KPIs and business outcomes.

- Drive continuous improvement of deployed agents based on feedback, telemetry, and usage patterns.

Skills Required:

- Business process mapping and facilitation.

- Design thinking and value-stream analysis.

- KPI and ROI definition.

- Strong communication and training skills.

- Familiarity with AI capabilities, limitations, and responsible-use practices.

- Change management and adoption planning.

- Ability to work with non-technical business stakeholders.

- Understanding of agentic AI concepts (multi-step reasoning, tool use, orchestration, memory, autonomy levels).

- Experience designing or contributing to AI agent workflows or automation solutions.

- Prompt engineering and prompt orchestration for task execution.

- Ability to translate business requirements into agent specifications and interaction flows.

- Familiarity with AI agent frameworks and platforms (e.g., Copilot Studio, Lang Chain, Semantic Kernel, or similar).

- Knowledge of API integration concepts, data flows, and system interactions.

- Ability to define evaluation frameworks for agent quality, accuracy, and reliability.

- Awareness of AI governance, safety, and risk controls specific to autonomous or semi-autonomous systems.