Project Manager
Exp: 3-6 Years
24 March 2026
Denver, USA
Role Overview
We are looking for a highly motivated Project Manager with hands-on experience building RAG-based, autonomous AI systems. The ideal candidate has strong Python skills, a solid grasp of modern LLM architectures, and practical experience delivering production-grade Agentic AI, Conversational AI, and Semantic Search solutions with a focus on performance, observability, and cost efficiency.
Key Responsibilities
Oversee Agentic AI system development from POC to production
Manage timelines, resources, and cross-functional team coordination
Define project scope, requirements, and success metrics
Monitor RAG pipeline implementation and deployment progress
Track performance metrics, costs, and operational efficiency
Ensure stakeholder alignment and communication
Identify and mitigate risks and blockers
Facilitate collaboration between engineering, data, and product teams
Required Skills
3–6 years of hands-on experience in project management, program delivery, or operations
Strong proficiency in stakeholder communication and cross-functional leadership
Experience with Agile methodologies (Scrum, Kanban) and sprint management
Solid understanding of project planning, scheduling, and resource allocation
Hands-on experience with project management tools such as Jira, Asana, Monday.com, or Microsoft Project
Practical exposure to risk management, issue tracking, and dependency mapping
Experience managing product launches, digital transformation initiatives, or complex deliverables
Working knowledge of budget management, cost tracking, and financial reporting
Advanced / Nice-to-Have Skills: AI Project Manager
Strategic Fine-Tuning Oversight: Evaluating the ROI and resource allocation for LoRA/QLoRA vs. RAG-based solutions.
AI Governance & Compliance: Implementing safety frameworks (Guardrails.ai, NeMo) and managing ethical/bias risks.
AI FinOps & Performance Metrics: Tracking and optimizing LLM unit economics (cost per token), latency, and throughput.
Agentic Workflow Orchestration: Managing the lifecycle of multi-step AI agents using frameworks like LangGraph or LangChain.
Data Stewardship & BI: Leveraging analytics dashboards to monitor model drift, accuracy, and business impact KPIs.
Technical Bridging: Translating complex technical constraints into clear stakeholder roadmaps and risk mitigation plans.
What We're Looking For
Reliability-First Mindset: Solving for AI hallucinations and system stability within project timelines.
Innovation vs. Delivery: Balancing experimental AI research with production-ready deployment standards.
Champion for Explainability: Passion for building transparent, ethical, and scalable autonomous systems.
Risk Orchestration: Proactively managing the unique uncertainties and technical debt of LLM lifecycles.