AI Project Manager

• Own end-to-end project delivery for AI-driven products including digital agents agent assist QA automation and analytics initiatives.
• Translate businessobjectivesand client requirements into clear AI project plans milestones and success metrics.
• Partner with the Customers and internal stakeholders to define requirements for LLM-based systems prompt strategies integrations and workflows.
• Provide cleardirectionofrequirements and expected outcomes to theengineering team.
• Manage execution of agentic workflows including tool-calling RAG pipelines context engineering and feedback loops.
• Coordinate cross-functional teams to ensuretimely high-quality releases acrossAI agent platforms.
• Coordinate AI solution integration into and withcloud-based contact center platforms ( Amazon Connect Genesys).
• Oversee prompt lifecycle management including iteration evaluation and performance tuning in production environments.
• Partner with QA and analytics teams to define andmonitorAI evaluation frameworks (accuracy containment escalation quality hallucination risk sentiment bias).
• Act as a client-facing projectlead managing timelines expectations risks and communications for enterprise deployments.
• Support incident response and continuous improvement efforts related to AI behavior data quality and system reliability.
• Maintain documentation for AI workflows evaluation plans escalation logic and model updates.
• Stay current on AIPMbest practices agentic design patterns and emerging tools relevant toHumachsplatform and client needs.

CORE COMPETENCIES

AI & LLM Foundations

• Working knowledge of Large Language Models (LLMs) including training phases inference and model limitations.
• Understanding ofprompt engineering principles and methods.
• Familiarity with open vs. closed-source model considerations cost management and production tradeoffs.

Agentic Systems & Workflow Design

• Experience supporting agenticworkflowsthat reason act and iterate toward defined goals.
• Understanding ofcontext engineering including system prompts memory and retrieval-augmented generation (RAG).
• Exposure totool-calling concepts and external API orchestration to support AI decision-making.

Evaluation & Quality Management

• Ability to define and interpret AIevaluationmetrics including qualitative and quantitative measures.
• Experience partnering with teams on human-in-the-loop review annotations and feedback ingestion.
• Understanding ofcommon AI failure modes (hallucinations bias drift) and mitigation strategies.

Technical & Platform Collaboration

• Comfort working cross-functionally with engineers on APIs cloud infrastructure and integrations.
• Familiarity with contact center and customer experience platforms and workflows.
• Ability to translate technical AI concepts into clear project plans and client-facing explanations.

Product Project & Client Leadership

• Strong project management discipline including planning risk management dependency tracking anddeliveryaccountability.
• Client-facing communication skills with the ability to manage expectations across technical and non-technical stakeholders.
• Experience balancing speed quality and scalability in enterprise environments.