AI Project Manager / AI Delivery Manager

OKAYA INFOCOM; New York (NY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

OKAYA INFOCOM is seeking an AI Project Manager to lead the execution of AI and Generative AI initiatives across multiple U.S. locations (Minneapolis, MN; Cincinnati, OH; NYC; NJ).

You will manage end-to-end delivery, coordinate engineers, data teams, QA, UX, and product teams, and partner with architects to translate business requirements into technical plans. The role demands 5–8 years in software project management and 2–3 years in AI-related delivery, with strong knowledge of LLMs, RAG, AI

Qualifications

• 5–8 years of experience managing software development or digital transformation projects.
• 2–3 years delivering AI, Machine Learning, or Generative AI projects.
• Experience working in a spec-driven development environment.
• Strong understanding of LLMs, AI agents, Retrieval-Augmented Generation (RAG), prompt engineering concepts, and AI application architectures.
• Experience with vector databases, embeddings, and AI orchestration frameworks.
• Experience with Agile methodologies and tools such as Jira and Confluence.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Strong understanding of REST APIs, system integrations, and modern software development lifecycles.
• Excellent stakeholder communication and coordination skills.
• Experience managing cross-functional teams of 5–15 members.

Responsibilities

• Manage end-to-end delivery of AI and Generative AI projects.
• Create project plans, sprint schedules, release roadmaps, and milestone tracking.
• Coordinate AI engineers, software developers, data engineers, QA, UX, and product teams.
• Work with solution architects to translate business requirements into technical execution plans.
• Monitor project progress, risks, dependencies, budgets, and resource allocation.
• Facilitate Agile ceremonies including sprint planning, stand-ups, reviews, and retrospectives.
• Track project KPIs and communicate status to stakeholders.
• Ensure timely delivery while maintaining quality and governance standards.
• Coordinate UAT, production releases, and post-deployment support.
• Drive continuous process improvements and AI delivery best practices.

Skills

Project management

AI project delivery

Agile methodologies

Stakeholder communication

Cross-functional leadership

Technical discussions with architects

Tools

Jira

Confluence

Cloud platforms (AWS/Azure/GCP)

Job description

AI Project Manager / AI Delivery Manager in Minneapolis ,MN / Cincinnati OH/NYC/NJ Full Time

Position Overview

We are looking for a technically strong AI Project Manager to lead the execution of AI and Generative AI initiatives. This role will manage project delivery, coordinate cross-functional teams, and work closely with AI engineers, architects, product managers, and business stakeholders to ensure successful implementation of AI solutions.

The ideal candidate combines solid project management experience with a strong understanding of AI technologies and software engineering practices. While not expected to build AI models, they should be comfortable discussing technical designs, managing delivery risks, and translating business requirements into execution plans.

Key Responsibilities

• Manage end-to-end delivery of AI and Generative AI projects.
• Create project plans, sprint schedules, release roadmaps, and milestone tracking.
• Coordinate AI engineers, software developers, data engineers, QA, UX, and product teams.
• Work with solution architects to translate business requirements into technical execution plans.
• Monitor project progress, risks, dependencies, budgets, and resource allocation.
• Facilitate Agile ceremonies including sprint planning, stand-ups, reviews, and retrospectives.
• Track project KPIs and communicate status to stakeholders.
• Ensure timely delivery while maintaining quality and governance standards.
• Coordinate UAT, production releases, and post-deployment support.
• Drive continuous process improvements and AI delivery best practices.

Required Qualifications

• 5–8 years of experience managing software development or digital transformation projects.
• 2–3 years of experience delivering AI, Machine Learning, or Generative AI projects.
• Experience working in a spec-driven development environment
• Strong understanding of LLMs, AI agents, Retrieval-Augmented Generation (RAG), prompt engineering concepts, and AI application architectures.
• Experience with vector databases, embeddings, and AI orchestration frameworks.
• Experience working with Agile methodologies and tools such as Jira and Confluence.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Strong understanding of REST APIs, system integrations, and modern software development lifecycles.
• Excellent stakeholder communication and coordination skills.
• Experience managing cross-functional teams of 5–15 members.

Preferred Skills

• Exposure to MLOps and AI deployment pipelines.
• Knowledge of Python-based AI ecosystems (without requiring hands‑on coding expertise).
• Experience in Banking, Financial Services, Healthcare, or Retail.
• Scrum Master or PMP certification is a plus.

Ideal Candidate

• Strong technical aptitude with the ability to engage confidently with architects and AI engineers.
• Excellent project planning, prioritization, and execution skills.
• Comfortable managing ambiguity in fast-evolving AI programs.
• Strong ownership mindset and proactive problem-solving ability.
• Effective communicator who can bridge business and technical teams.
• Collaborative leader capable of motivating multidisciplinary teams and driving successful project outcomes.

What this person should know technically (without being an AI engineer)

A good candidate should be able to:

• Understand the difference between traditional ML and Generative AI.
• Explain concepts such as LLMs, RAG, AI agents, embeddings, vector databases, MCP, and prompt engineering at a high level.
• Read architecture diagrams and participate in technical design discussions.
• Understand APIs, microservices, cloud architecture, and system integrations.
• Manage AI project risks such as hallucinations, model evaluation, latency, security, and cost.
• Work with Git, CI/CD concepts, and deployment pipelines, even if they are not hands‑on.