Role overview : The Technical Project Manager (AI, Data & Automation) will lead the end-to-end planning, execution, and delivery of AI-driven data products, reporting dashboards, and automation solutions. This role uniquely blends agile delivery leadership, technical context engineering, and AI solution orchestration — ensuring that LLMs, agents, and data systems work in harmony to drive business impact.
You will collaborate with product owners, data engineers, AI developers, and business stakeholders to ensure dashboards, models, and AI agents are delivered with precision, scalability, and measurable outcomes. Beyond project execution, you will help evolve internal best practices for AI product delivery, including context design, model evaluation, and agent observability.
Key Responsibilities :
Project & Delivery Management
- Define, plan, and manage delivery timelines for AI, data, and BI projects (Power BI, Tableau, Datorama, etc.)
- Lead sprint planning, UAT cycles, and release management using Agile (Scrum / Kanban) frameworks
- Maintain delivery documentation — user stories, acceptance criteria, dependency trackers, and sprint reports
- Monitor KPIs such as velocity, sprint burndown, delivery variance, and model deployment readiness
Technical Context & AI Orchestration
Collaborate with data scientists, prompt engineers, and developers to convert business problems into LLM workflowsBuild and manage context frameworks, prompt libraries, and retrieval pipelines using LangChain, LlamaIndex, or similar orchestration layersTrack model performance, latency, and response accuracy through LangFuse, Weights & Biases, or PromptLayer for observability and evaluationDesign AI agent architectures that integrate with data systems (SQL, APIs, cloud data stores) and downstream visualization toolsSupport technical feasibility assessments, architecture discussions, and proof-of-concept builds for AI agents and copilotsPartner with engineering to implement guardrails, prompt validation, and feedback loops for production-grade AI reliabilityStakeholder & Cross-Functional Alignment
Act as the bridge between business teams (Marketing, PMs, Strategy) and technical delivery squadsDefine clear project scopes, delivery milestones, and stakeholder reporting rhythmsFacilitate weekly progress syncs, demo reviews, and cross-team retrospectivesManage change control, risk tracking, and communication for multi-project programsQuality, UAT & Continuous Optimization
Design and coordinate UAT plans for dashboards, data pipelines, and AI agentsValidate AI performance against qualitative (accuracy, relevance, consistency) and quantitative (latency, token cost, precision) benchmarksCapture feedback loops for fine-tuning models, retraining datasets, or updating prompt templatesEnsure that delivery documentation and dashboards meet expected usability and performance standardsStrategic Enablement & Practice Building
Define internal standards for AI project delivery, including experimentation logs, evaluation metrics, and documentation templatesContribute to developing multi-client AI reporting frameworks and automation playbooksBenchmark AI orchestration tools (LangChain, Haystack, Dust, OpenDevin) and delivery processes for continuous improvementPartner with leadership on roadmap prioritization, team resourcing, and delivery governanceMentor junior PMs and BSAs to operate effectively within AI + Data delivery contextsRequired Skills & Experience
6 to 10 years of relevant experienceProven understanding of data pipelines, APIs, and BI tools (Power BI, Datorama, Tableau)Working familiarity with AI / LLM toolchains — LangChain, LangFuse, LlamaIndex, OpenAI, Anthropic, or Azure OpenAIUnderstanding of prompt engineering, context window optimization, and RAG (Retrieval-Augmented Generation) designExperience coordinating with teams using Python, FastAPI, SQL, and cloud platforms (AWS / Azure)Exposure to model monitoring, prompt versioning, and evaluation pipelinesAbility to interpret structured / unstructured data and manage integrations between AI and data visualization layersStrong experience leading AI, data, or analytics product delivery using Agile / ScrumSkilled in backlog management, sprint planning, and multi-project coordination.Proficiency with PM tools — Jira, ClickUp, Monday.com, Confluence, or similar.Excellent skills in cross-functional communication, requirements clarity, and deliveryCertifications : PMP or Prince IIPrior experience delivering AI agents, chatbots, copilots, or automation systemsExposure to AI evaluation pipelines (LangFuse, Traceloop, Arize) and prompt management toolsExperience in marketing analytics, optimization platforms, or enterprise data systems