Product Manager – AI Framework & Workflow Automation

The Product Manager – AI Framework & Workflow Automation owns the foundational AI and workflow infrastructure that enables ENSEK’s agentic AI capabilities. This role is responsible for defining and delivering the strategy, roadmap, and outcomes for ENSEK’s core AI framework — including agent orchestration, LLM operations, cost and performance monitoring, and workflow automation. Working at the intersection of engineering, data science, and commercial product teams, you’ll ensure ENSEK’s AI infrastructure is scalable, secure, cost‑effective, and production‑ready, and that it meaningfully accelerates product teams.

Key Accountabilities & Responsibilities

• Own the strategy, vision and roadmap for the AI framework and agentic workflow platform, defining how agent orchestration, LLM operations, and workflow automation capabilities are built and evolved to support case management and other product domains.

• Define and own the LLM ops framework — establishing cost monitoring, usage metrics, model performance tracking and observability standards across all LLM-powered features, ensuring production-grade reliability and cost efficiency.

• Bridge the gap between data science capabilities and engineering infrastructure, working closely with other teams to ensure foundational AI tooling is production ready.

• Build and prioritise the backlog for AI framework capabilities including agent orchestration, prompt management, evaluation pipelines and model routing — making evidence-based trade-off decisions on build vs. buy with engineering leads.

• Define governance, security and auditing requirements for agentic systems in collaboration with identity and access management stakeholders.

• Represent platform AI capabilities to stakeholders across the business, communicating roadmap progress, risks and outcomes clearly to senior leaders and commercial partners.

• Create and maintain PRDs and specifications for AI framework capabilities, working with engineering to validate technical approaches and define measurable success criteria.

Key Outcomes

• Agentic AI framework in production, with defined ownership of agent orchestration, LLM ops, cost monitoring and metrics across all LLM-powered product features.

• Case management team unblocked from AI infrastructure dependencies, with MVP ready for beta testing within 3–4 months.

• LLM cost and performance dashboards live, providing real-time visibility into model usage, quality and spend across the platform.

• Clear roadmap and OKRs for the AI framework domain, reviewed regularly with engineering and product leadership.

• Defined governance model for agentic systems including security, auditing and access control requirements.