Technical Product Manager AI Agentic systems

Black Duck Software, Inc. helps organizations build secure, high-quality software, minimizing risks while maximizing speed and productivity. Black Duck, a recognized pioneer in application security, provides SAST, SCA, and DAST solutions that enable teams to quickly find and fix vulnerabilities and defects in proprietary code, open source components, and application behavior. With a combination of industry-leading tools, services, and expertise, only Black Duck helps organizations maximize security and quality in DevSecOps and throughout the software development life cycle.

Technical Product Manager – AI / Agentic Systems (Polaris)

Role Overview:

We are looking for a Technical Product Manager (TPM) to help scale and operationalize AI- and agent-enabled capabilities within Polaris, our flagship application security platform.

This individual will serve as a force multiplier for the broader PM team—bringing strong technical depth in AI/agentic systems, while supporting senior PMs in shaping, validating, and shipping differentiated capabilities. The goal is to elevate our AI maturity across the team and accelerate delivery of high-impact, AI-native workflows.

Key Responsibilities:

1) Drive AI / Agentic Product Capabilities

• Shape and prioritize AI-native features, including:

• Agent-based workflows (multi-agent orchestration, task decomposition)

• AI-assisted triage, remediation, and developer guidance

• LLM-powered analysis, summarization, and reasoning

• Partner with engineering to translate emerging AI concepts into production-ready product features

• Evaluate build vs. buy decisions across models, tools, and platforms

2) Act as the AI “Center of Gravity” for the PM Team

• Support other Polaris PMs in:

• Framing AI opportunities in their domains (SAST, SCA, Dynamic, ASPM)

• Converting customer problems into AI-enabled workflows

• Define repeatable patterns and best practices for AI features across the platform

• Help establish internal standards for:

• Prompting strategies

• Evaluation frameworks

• Agent design patterns

3) Bridge Product, Engineering, and Applied AI

• Work deeply with R&D on:

• LLM integrations, APIs, and performance characteristics

• Agent orchestration and workflow design

• Data pipelines and context enrichment (code, repos, findings)

• Ensure AI features align with real developer workflows (IDE, CI/CD, PR flows)

4) Accelerate Prototyping and Customer Validation

• Rapidly prototype concepts (with engineers or independently via AI tools)

• Partner with design, sales, and customers to:

• Validate workflows early

• Iterate quickly using real-world feedback

• Shorten cycle time from idea → prototype → validated feature

5) Contribute to AI Strategy and Positioning

• Help define how Polaris differentiates in an AI-first AppS