AI Workbench Product Lead

Univers combines global operating scale, deep industrial intelligence, and recognition from the institutions shaping energy, technology, and sustainability. The platforms built for the last decade were designed to monitor. Univers was built to act — not just report.

Univers operates at global scale:

• 1,070 GW+ Energy assets under AI management
• 450M+ Connected sensors and devices
• 800+ Enterprise customers
• Leader Gartner Magic Quadrant Leader

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Job Summary

As a product leader pioneering our Physical AI platform, you will spearhead the product vision, strategy, and roadmap for the AI Agent Workbench. The AI Workbench is the core enablement platform designed to transform how internal engineering teams and external customers build, deploy, manage, and scale AI Agents and autonomous workflows.

In this role, you will define the end-to-end product specifications, developer experience, and orchestration capabilities required for agentic automation, RAG, and AI-driven operational decision-making. You will bridge complex AI capabilities (such as LLM orchestration, Model Context Protocol, and agentic tools) with real-world physical infrastructure workflows, driving platform innovation and setting the industry standard for enterprise AI engineering.

Responsibilities:

AI Workbench Strategy: Define the multi-year product vision, strategic positioning, and feature roadmap for the AI Workbench, establishing it as the primary builder platform for AI Agents and automation workflows. Use Case Expansion: Identify and prioritize high-impact AI enablement opportunities across internal operations and external enterprise customer ecosystems (e.g., energy orchestration, predictive maintenance, automated operations). Agent Builder & Studio: Define product requirements for low-code/pro-code agent builder interfaces, prompt engineering environments, agentic workflow visualizers, and evaluation sandboxes. API & Integration Architecture: Partner with principal architects to shape developer-facing APIs, SDKs, and tool integration specifications (e.g., Model Context Protocol / MCP, custom tool-calling frameworks). Multi-agent Orchestration: Lead product capabilities around multi-agent coordination. Enterprise AI Governance: Define guardrails for responsible AI, including fine-grained tool permissioning, role-based access control (RBAC), data privacy, and safe human-in-the-loop (HITL) approval workflows. Observability & Evaluation: Specify capabilities for agent tracing, token usage/cost analytics, latency monitoring, prompt caching performance, and automated agent evaluation (evals).

Cross-Functional Leadership & Operational Excellence: Cross-Functional Execution: Collaborate closely with AI Research, Platform Infrastructure, Frontend/Backend Engineering, UX Design, and Solutions Engineering to convert ambiguous goals into shipped, high-quality products. Product Enablement & Adoption: Drive platform adoption across internal engineering pods and external developer communities, establishing best practices, documentation, and reference architectures. Customer Feedback Loop: Engage directly with customer leadership and forward-deployed engineers to synthesize field feedback into platform improvements, maintaining high satisfaction and rapid issue resolution.

Qualifications:

• 10+ years of product management or software development, with a passion for AI and language technologies.
• Proven track record of delivering major impact.
• Strong technical background in software engineering, backend architecture, distributed systems, or platform engineering (prior hands-on development experience in Python, TypeScript, or Go is highly valued).
• Experience in design and development of large-scale distributed, high concurrency, high load, high availability systems.
• Strong understanding of software architecture, distributed systems, APIs, and platform engineering principles.
• Hands-on experience with modern CI/CD, DevOps, and software delivery practices.
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Software Engineering, or a related technical field.

AI And Agentic Engineering Experience:

• Practical experience working with large language models (LLMs), AI APIs, and retrieval-augmented generation (RAG) architectures.
• Strong conceptual and practical background in Loop Engineering—designing robust closed-loop execution systems, agentic retry/reflection loops, dynamic error-handling cycles, and Human-In-The-Loop (HITL) approval workflows.
• Experience in implementing Recursive Self-Improvement (RSI) or self-correcting agent architectures (e.g., automated prompt optimization, self-critique/reflection cycles, multi-turn reasoning loops, and automated agent evaluation loops) to continuously enhance agent accuracy and task completion.
• Experience building AI-powered applications, agents, or workflow automation solutions.
• Familiarity with frameworks and tooling such as LangChain, LangGraph, MCP, vector databases, orchestration frameworks, and agent-based architectures.
• Strong understanding of AI governance, security, observability, and responsible AI practices.

Personal Attributes:

• Builder mindset with a passion for experimentation and innovation.
• Strong problem-solving and systems-thinking abilities.
• Ability to influence without authority and drive technology adoption across teams.