Job Opportunities API

Role Summary our company is hiring an AI Product Manager to own the operational line of the Technology team - task assignment, delivery cadence, performance tracking, documentation, and discipline - while keeping AI product delivery on track. This role runs the day-to-day engine of the team so engineers and data scientists can focus on building. The technical/architecture direction stays with the Technical Head; this role partners with that line but owns how work is planned, tracked, and shipped. The role combines understanding stakeholder requirements and leading client technical meetings , a hands-on understanding of how AI systems are deployed and how AI products are managed , and a research mandate to advance D proprietary models and agentic layers . These capabilities matter far more than years on a CV, which is why we are opening this at a mid-level (2-5 years) for a sharp, client-facing operator who can grow with the company. 2. Core Competencies Candidates will be assessed against the following areas: •        Stakeholder requirements & client technical meetings - leads client and stakeholder technical meetings with confidence, asks the right questions to surface real needs and constraints, captures and documents requirements clearly, translates them into specs and tasks for the team, and manages expectations and scope throughout delivery. Comfortable being the technical face of in front of clients. •        AI systems deployment - practical understanding of the AI/ML delivery lifecycle: moving models and AI features from prototype to production, MLOps basics (versioning, retraining, monitoring, rollback), data pipelines, evaluation, and the operational risks of running AI in production. Able to spot delivery blockers early and hold an engineering team accountable to a deployment plan. •        AI product management - translating business goals into a prioritised backlog, writing clear specs and acceptance criteria, running an analysis-before-development workflow, managing scope against client commitments, and owning roadmap, releases, and stakeholder communication for AI products. •        Applied AI research direction - able to read the landscape of models and agentic techniques, frame research objectives, and steer the team toward greater in-house capability - reducing dependence on third-party models and building proprietary models and agentic layers into our company AI systems. 3. Key Responsibilities •        Stakeholder requirements & client meetings - lead client and stakeholder technical meetings, elicit and document requirements, translate them into clear specs and tasks, manage scope and expectations, and act as the technical point of contact between the client, leadership, and the team. •        Team & operations management - assign and balance work across the Technology team, run the weekly delivery cadence, remove blockers, and own team discipline and ways of working. •        Delivery & product ownership - maintain the backlog and roadmap for the Paula and -Hub products, enforce the analysis-before-development workflow, and ensure no client commitments are made without proper scoping. •        Research & proprietary capability - set and drive research objectives to grow own models and agentic layers, run a pipeline of experiments alongside delivery, and progressively move the AI systems from off-the-shelf components toward proprietary, in-house capability. •        Performance & KPIs - track weekly KPIs, run progress reviews, evaluate performance, and surface risks to leadership early. •&nb