We are looking for a Senior Product Manager / Director (Individual Contributor) to lead complex, high-impact product areas across AI-powered enterprise platforms.
This role is suited for someone who operates as a senior IC with end-to-end ownership—driving product strategy, shaping system design, and delivering high-quality outcomes in ambiguous problem spaces.

You will work at the intersection of AI/LLM systems, search, and enterprise workflows, building copilot-style and agentic AI experiences that enable users to interact with systems through natural language, contextual insights, and intelligent assistance. You will also be responsible for designing scalable, extensible platform systems that serve as the foundation for AI-driven capabilities across the product.

Icertis is the global leader in AI-powered contract intelligence. The Icertis platform revolutionizes contract management, equipping customers with powerful insights and automation to grow revenue, control costs, mitigate risk, and ensure compliance - the pillars of business success. Today, more than one third of the Fortune 100 trust Icertis to realize the full intent of millions of commercial agreements in 90+ countries.

Experience

• 8–12 years in Product Management

Experience in at least 2 of the following areas (with demonstrated product ownership):

• AI/ML or LLM-based products, including copilot-style or agent-driven experiences
• Enterprise SaaS platforms
• Search, data, or workflow systems

Core Skills

Strong problem structuring and analytical thinking.

Solid understanding of:

• AI/LLM product patterns (search, reasoning, summarization)
• Designing copilot and agent-based systems (assist + automate)
• Agentic AI patterns — tool-use, planning loops, multi-agent coordination, and autonomous execution

• System trade-offs in distributed or data-heavy environments

Familiarity with:

• Multi-step reasoning workflows and orchestration patterns
• Human-in-the-loop vs autonomous execution models
• Designing for trust, observability, and failure handling in AI systems

• Building scalable, multi-tenant platform systems with reusable components and extensible APIs

Ability to:

• Operate at both system and feature level
• Write clear, structured product documentation

• Work hands-on with engineering teams on architecture and scale decisions

Operating Style

• High ownership, strong execution focus
• Comfortable navigating ambiguity and evolving requirements
• Influences without authority; drives outcomes independently

Success in This Role

• Takes ownership of critical product areas end-to-end
• Brings clarity to ambiguous problems and drives execution
• Builds intuitive copilot experiences and reliable agentic workflows
• Designs scalable systems that become the foundation for multiple product capabilities
• Improves product quality across usability, trust, and impact
• Enables systems that not only answer questions—but plan, act, and complete meaningful tasks autonomously

1. Problem Solving & Product Strategy

• Own ambiguous problem areas and define clear product direction
• Break down complex workflows into structured, solvable components

• Drive roadmap decisions grounded in business impact, feasibility, and scale

2. AI & Agentic Experience Ownership

Lead product thinking across:

• Copilot-style interfaces (natural language, conversational UX)
• AI-powered capabilities such as retrieval, and reasoning
• Agentic workflows — autonomous task execution, multi-agent orchestration, tool-use patterns, and planning-execution loops

Design systems that:

• Enable context-aware exploration and decision support
• Support multi-step reasoning, iterative workflows, and goal-driven agent behavior
• Combine assistance (copilot) with autonomous action (agents)
• Allow agents to plan, decompose tasks, select tools, and execute reliably

Define how systems:

• Transition from insight → recommendation → autonomous action
• Balance autonomy vs control in agent behavior
• Ensure reliability, guardrails, observability, and explainability

Drive key trade-offs:

• Accuracy vs latency
• Cost vs performance
• Automation vs human-in-the-loop control

• Agent autonomy vs enterprise governance

3. Hands-on Execution & Scalable System Thinking

Create clear product artifacts (PRDs, frameworks, decision docs).

Go deep into:

• Feature definition and edge cases
• System behavior, failure modes, and recovery patterns
• End-user experience, clarity, and trust

Shape:

• Interaction patterns (response flows, reasoning transparency)
• Agent behaviors (triggering, execution boundaries, fallback logic)
• How outputs are grounded, verifiable, and actionable

Design for scale:

• Build reusable, extensible platform components that serve multiple product areas
• Think in terms of shared primitives, APIs, and composable capabilities
• Ensure systems are designed for multi-tenant, high-volume enterprise environments

Partner closely with engineering on system architecture, scalability, and implementation decisions.

4. Cross-Functional Leadership

• Work closely with engineering, design, and business teams
• Align stakeholders and drive decisions across functions
• Execute without formal authority, relying on influence and clarity

5. Customer-Centric Thinking

• Translate real-world problems into scalable solutions
• Balance domain depth with platform generalization
• Ensure solutions are practical, reliable, and enterprise-ready