Technical AI Product Manager

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information

Key Responsibilities

• Define product vision, roadmap, and execution strategy for AI-driven products and platforms. 

• Partner with engineering, AI, data, and infrastructure teams to deliver scalable AI solutions. 

• Drive product discovery, requirements definition, prioritization, and go-to-market planning. 

• Collaborate on capabilities including model serving, orchestration, data pipelines, and evaluation. 

• Collaborate to define scalable architectures for AI apps~ RAG, Data integrations, and AI guardrails. 

• Translate customer and business needs into features and measurable outcomes. 

• Monitor product performance, adoption, reliability, and operational metrics. 

• Drive stakeholder alignment across business, engineering, security, compliance, and operations.

Required Qualifications

• 10+ years of Product Management experience, including experience working on AI products. 

• Strong technical understanding of systems design and thinking, data, platforms and modern software architectures. 

• Strong experience driving product launch strategy, GTM planning, and user adoption initiatives 

• Experience working with AI/ML systems, including concepts such as: 

• LLMs and Generative AI 

• Retrieval-Augmented Generation (RAG) 

• Vector Databases 

• Model serving and inference architectures 

• AI safety, governance, and guardrails 

• Data integration and orchestration pipelines 

• Ability to communicate effectively with both technical and non-technical stakeholders including Sr/Directors and VPs. 

• Strong analytical, problem-solving, and prioritization skills. 

Preferred Qualifications

• Experience with rapid AI prototyping, vibe coding workflows, and building evaluation or testing harnesses for LLM-based applications 

• Familiarity with GCP/AWS/cloud-native architectures and MLOps practices. 

• Experience working with experimentation frameworks, evaluation pipelines, or AI observability tooling. 

• Background working in highly cross-functional product and engineering environments. 

Success Criteria

• Outcome/KPI-driven product management mindset with experience driving feature prioritization, adoption, and measurable business impact.

• Strong product rigor with the ability to drive adoption and ROI through deep user understanding, cohort analysis, customer research, and industry/best-practice insights. 

• Excellent storytelling, communication, and narrative-building skills with the ability to articulate product vision, influence stakeholders, and galvanize cross-functional teams. 

• Strong orchestration and accountability management capabilities across client stakeholders, product, engineering, and business teams to drive alignment and execution.