Product Owner

Quantiphi; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Exposure to enterprise clients

Collaborative, learning-oriented文化

Job summary

Quantiphi seeks an experienced product leader to own the AI/ML product direction for agentic systems in a Chicago-based role. You will guide ideas from research to production, collaborating across research, engineering, architecture, and business stakeholders in a SAFe environment.

Responsibilities include shaping the vision for agent-based architectures, managing the research backlog, and translating advanced concepts into epics and specs for scalable deployments with CI/CD.

Qualifications

• 10–12 years of professional experience in product roles for technical/data products.
• Practical knowledge of generative AI, large language models, agent workflows and development kits, RAG, vector search, and knowledge graph integration.
• Experience with SAFe practices including backlog refinement, sprint and PI planning, and cross-team dependency management.
• Understanding of Google Cloud Platform, Kubernetes, and GitHub Actions for cloud infrastructure and CI/CD.
• Ability to discuss data pipelines, APIs, latency, agent orchestration, and model inference scaling.
• Track record translating executive strategy and data science research into actionable work for cross-functional teams.

Responsibilities

• Set the product vision and roadmap, prioritizing agent-based architectures and workflows with meaningful business impact.
• Manage the research and innovation backlog, weighing experimental risk, technical feasibility, and expected value.
• Turn concepts such as tool-using agents, retrieval-augmented generation (RAG), knowledge graphs, and multi-agent coordination into epics, user stories, acceptance criteria, and specifications.
• Partner with architects and engineers on scalable, resilient, and secure cloud deployments, including iterative delivery and CI/CD practices.
• Guide initiatives from hypothesis and proof of concept through evaluation, production readiness, rollout, and adoption.
• Define measures for reliability, task completion, cost, and user impact, and use feedback to refine or advance solutions.

Skills

Generative AI

LLMs

Agent workflows

RAG

Vector search

Knowledge graphs

SAFe practices

APIs

Model inference scaling

Tools

Google Cloud Platform

Kubernetes

GitHub Actions

CI/CD

Job description

Role overview

Own the product direction for AI and machine learning initiatives centered on agentic systems, translating emerging research into products that can operate at enterprise scale. Work across research, engineering, architecture, operations, and business stakeholders in a Scaled Agile Framework (SAFe) environment, guiding ideas from early experiments through production and adoption. The role is based in Chicago and seeks an experienced product leader.

Responsibilities

• Set the product vision and roadmap, prioritizing agent-based architectures and workflows with meaningful business impact.


• Manage the research and innovation backlog, weighing experimental risk, technical feasibility, and expected value.


• Turn concepts such as tool-using agents, retrieval-augmented generation (RAG), knowledge graphs, and multi-agent coordination into epics, user stories, acceptance criteria, and specifications.


• Partner with architects and engineers on scalable, resilient, and secure cloud deployments, including iterative delivery and CI/CD practices.


• Guide initiatives from hypothesis and proof of concept through evaluation, production readiness, rollout, and adoption.


• Define measures for reliability, task completion, cost, and user impact, and use feedback to refine or advance solutions.

Requirements

• 10–12 years of professional experience, including substantial work as a Product Owner, Technical Product Manager, or product lead for technical or data products.


• Practical knowledge of generative AI, large language models, agent workflows and development kits, RAG, vector search, and knowledge graph integration.


• Experience with SAFe practices, including backlog refinement, sprint and PI planning, and cross-team dependency management.


• Understanding of Google Cloud Platform, Kubernetes, and GitHub Actions for cloud infrastructure and CI/CD.


• Ability to contribute to discussions about data pipelines, APIs, latency, agent orchestration, and model inference scaling.


• Track record of translating executive strategy and data science research into actionable work for cross-functional teams.

Benefits and work setup

The position is full time and based in Chicago. The listing describes exposure to enterprise clients and current AI, machine learning, data, and cloud work, alongside a collaborative, learning-oriented culture.