Senior Product Manager

Description:

About the Role
We are looking for a Senior Product Manager for our AI Platform to drive the execution and delivery of Aurigo's proprietary AI foundation model, agent framework, and the AI platform layer that powers our products. This is a hands-on product role that operates at the intersection of AI engineering, enterprise software, and capital program workflows. You will partner closely with engineering, data science, and design teams to define, build, and ship AI capabilities that are safe, measurable, and impactful in enterprise environments.  Unlike the Director role which is focused on strategy, vision, and stakeholder leadership, this role is about deep execution. You will own specific product areas end to end, write detailed requirements, drive sprint-level delivery, and ensure that AI products meet well-defined quality and safety standards before reaching customers. You will also engage with customers and internal product teams to ground your decisions in real-world use cases.

Roles and Responsibilities

1. Own and execute the product roadmap for assigned areas of the AI platform, including the ingestion and context layer, orchestration layer, agent framework components, and the Copilot experience across all product lines.
2. Write detailed product requirements, user stories, and acceptance criteria for AI features, and drive sprint planning, backlog grooming, and delivery tracking in close partnership with engineering and machine learning teams.
3. Define and maintain the evaluation framework for assigned AI features including offline evals, benchmark metrics, regression tests, and production monitoring. No agent or model capability ships without defined success criteria.
4. Own the responsible AI execution process at the feature level, including guardrail testing, safety checklists, bias review, and auditability verification before any AI capability reaches customers.
5. Serve as the internal AI platform expert for Senior Product Managers across all product lines, providing guidance on how to correctly integrate, specify, and evaluate AI capabilities within their respective workflows.
6. Contribute to the model management and MCP server roadmap by gathering data on provider performance, cost, and quality, and by identifying integration opportunities and validating tool specifications.
7. Engage with enterprise customers to gather feedback on AI feature performance, understand adoption patterns, and translate insights into roadmap decisions.
8. Monitor the competitive landscape for relevant AI product developments, agent frameworks, and enterprise AI tooling, and bring insights into prioritization discussions.

Requirements:

Soft Skills

1. Technically credible with engineering and data science teams. Can engage substantively on prompt design, evaluation tradeoffs, RAG configurations, and agent behavior without needing to write code.
2. Clear and precise communicator. Able to write requirements, specs, and documentation that engineering teams can act on without repeated clarification.
3. Customer empathy. Listens carefully to enterprise customer feedback and translates nuanced qualitative input into actionable product decisions.
4. High ownership mindset. Takes accountability for feature quality, safety, and customer outcomes, not just delivery of scope.
5. Structured problem-solver. Breaks ambiguous AI product challenges into well-defined hypotheses, experiments, and evaluation plans.
6. Stays current with fast-moving developments in foundation models, agentic architectures, and enterprise AI deployment patterns

Hard Skills

1. Strong understanding of the AI product development lifecycle including data pipelines, prompt engineering, RAG architectures, fine-tuning, and agentic system design.
2. Experience designing and running AI evaluation frameworks including offline evals, human evaluation pipelines, and production monitoring for AI features.
3. Familiarity with responsible AI principles including human-in-the-loop design, fairness considerations, auditability, and guardrail implementation in enterprise settings.
4. Working knowledge of model context protocol or equivalent tool-use and integration frameworks for agentic systems.
5. Ability to define and track AI-specific product metrics including task completion rates, hallucination rates, model latency, cost per inference, and user satisfaction signals.

Technical Skills

1. Familiarity with major foundation model providers and the practical tradeoffs among them in terms of capability, cost, latency, context window, and data privacy.
2. Working knowledge of vector databases, semantic search, and embedding-based retrieval systems relevant to RAG product design.
3. Familiarity with AI evaluation tooling such as LLM eval libraries, RAGAS, or equivalent frameworks for measuring AI system performance.
4. Understanding of AI orchestration patterns including ReAct loops, multi-agent coordination, and durable workflow execution for long-running agent tasks.
5. Proficiency with standard product management tools alongside AI-specific development and observability platforms.

Educational Qualifications

1. Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field. Advanced degree in a relevant discipline is an advantage.

Prior Experience

1. 7 to 10 years of experience in product management, with at least 2 to 3 years owning AI or machine learning product features in a B2B SaaS or enterprise software environment.
2. Demonstrated experience shipping AI features to production in enterprise contexts, including agents, copilots, predictive models, or recommendation systems.
3. Hands-on experience defining evaluation criteria and running evals for AI features, including benchmark design, human evaluation coordination, and production quality tracking.
4. Experience working directly with data science and machine learning engineering teams on model prototyping, prompt iteration, or deployment workflows.
5. Background in infrastructure, construction, capital program management, or other complex enterprise domains is a strong advantage.

About Aurigo

Aurigo provides AI-native platforms that help capital owners connect planning, construction, and operations in a single environment, improving decision-making and execution. Trusted by over 300 organizations across federal, state, and local government, manufacturing, data centers, energy and utilities, and life sciences, we manage more than $450 billion in capital programs across 40,000+ projects in North America. Built on 20+ years of domain expertise, Aurigo has received multiple industry awards and has been certified as a Great Place to Work for three consecutive years.

At Aurigo, we go beyond building technology—we shape the future. If you're excited to join a fast-growing company and collaborate with some of the brightest minds in the industry to solve real-world challenges, let's connect.