Technical Product Owner – Enterprise AI Platform

Altak Group Inc.; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Altak Group Inc. is seeking a Technical Product Owner for the Enterprise AI Platform (GenAI & LLMOps). The role covers end-to-end ownership of platform back-end capabilities, including AI/ML pipelines, risk-mitigated deployment, and scalable architecture.

You will collaborate with cybersecurity, legal, and compliance teams to ensure secure, compliant deployments across the enterprise. Remote-first with occasional visits to Virginia offices; highly technical, governance-focused leadership is

Qualifications

• 5+ years of explicit experience driving technical product ownership in large-scale enterprises.
• Proven track record delivering infrastructure components related to AI/ML pipelines, MLOps, or distributed backend systems.
• Experience navigating regulated industries with strong security/compliance focus (Healthcare, HealthTech, Health Insurance).

Responsibilities

• Own and execute the platform roadmap, backlog, prioritization matrix, and enterprise release plans.
• Scale LLM service orchestration, multi-model routing, token optimization, and retrieval (RAG) components.
• Translate compliance requirements into backend platform features ensuring secure deployment for regulated environments.
• Lead Scrum/Agile ceremonies and communicate progress to engineers and executive stakeholders.

Skills

Technical product ownership

AI/ML pipelines

MLOps

Agile/Scrum at scale

Stakeholder communication

Tools

Jira

Confluence

Job description

Position: Technical Product Owner - Enterprise AI Platform (GenAI & LLMOps)

Location: Remote (Once a month Visit in Virginia Office)

Employment Type: Full-time / Contract

We are seeking an elite, systems-thinking Technical Product Owner to orchestrate the lifecycle, scaling, and architectural evolution of our Enterprise AI Platform. This is not a front-end or basic UI workflow role. You will own the core backend engine the secure, unified, and highly optimized bedrock that enables hundreds of internal engineering, data science, and business units to deploy production-ready AI capabilities safely.

You will sit at the absolute intersection of cutting-edge Generative AI, cloud infrastructure, and rigorous compliance, transforming deep technical capabilities into robust, scalable platform services.

Key Responsibilities:

1. GenAI Capabilities & LLMOps Roadmap

• Own and execute the platform roadmap, backlog, prioritization matrix, and enterprise release plans.
• Support and scale foundational platform capabilities, including LLM service orchestration, multi-model routing, fallback mechanisms, and token optimization.
• Drive product strategy for advanced retrieval (RAG) components, including enterprise-wide vector search infrastructure, embeddings pipelines, and semantic data chunking strategies.
• Define platform patterns for autonomous, multi-step AI agent frameworks and secure tool-calling execution environments.

2. "Paved Road" Governance, Security & Compliance

• Partner heavily with Cybersecurity, Legal, and Compliance teams to enforce strict guardrails around Generative AI deployment.
• Bake programmatic guardrails into the platform for PII/PHI data masking, data leakage prevention, and model bias validation.
• Translate complex healthcare compliance frameworks (e.g., HIPAA, HITRUST, SOC2) directly into automated backend platform features, ensuring the most secure deployment path is the easiest path for internal developers.

3. Platform Unit Economics & Lifecycle Management

• Treat internal developers and data scientists as your true customers by delivering world-class internal APIs, SDKs, and model-hosting documentation.
• Optimize platform unit economics by aggressively tracking and managing token usage, model inference costs, and cloud compute footprints without sacrificing system performance.
• Ruthlessly balance the introduction of cutting-edge AI features with infrastructure scalability, technical debt reduction, and system reliability.
• Deconstruct complex architectural initiatives into crisp, rock-solid technical epics, user stories, and quantitative acceptance criteria.
• Lead vital Scrum/Agile ceremonies, including sprint planning, daily stand-ups, backlog refinement, and cross-team dependency mapping.
• Communicate roadmap progress, architectural risks, and delivery status with clarity to both deeply technical engineers and executive business stakeholders.

Requirements:

• Experience: 5+ years of explicit experience driving technical product ownership, technical product management, or platform management within a large-scale enterprise environment.
• Domain Expertise: Proven track record delivering infrastructure components specifically related to AI/ML pipelines, MLOps, deep enterprise data platforms, or highly distributed backend systems.
• Regulated Industry DNA: Direct experience navigating the data complexities and strict security architectures of highly regulated ecosystems (Healthcare, HealthTech, Health Insurance)
• Technical Literacy: While you do not need to write production code, you must possess the technical depth to challenge architectural decisions, review API contracts, and speak the native language of LLM engineers and Principal Architects.
• Compliance Awareness: Strong familiarity with data governance frameworks, security compliance standards (e.g., HIPAA, HITRUST), and AI safety/risk mitigation protocols.
• Agile Rigor: Exceptional mastery of Scrum/Agile frameworks at scale, with deep proficiency using enterprise tracking tools like Jira and Confluence to manage complex cross-team dependencies