Technical Project Manager

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 workflows
• Build and manage context frameworks, prompt libraries, and retrieval pipelines using LangChain, LlamaIndex, or similar orchestration layers
• Track model performance, latency, and response accuracy through LangFuse, Weights & Biases, or PromptLayer for observability and evaluation
• Design AI agent architectures that integrate with data systems (SQL, APIs, cloud data stores) and downstream visualization tools
• Support technical feasibility assessments, architecture discussions, and proof-of-concept builds for AI agents and copilots
• Partner with engineering to implement guardrails, prompt validation, and feedback loops for production-grade AI reliability

Stakeholder & Cross-Functional Alignment

• Act as the bridge between business teams (Marketing, PMs, Strategy) and technical delivery squads
• Define clear project scopes, delivery milestones, and stakeholder reporting rhythms
• Facilitate weekly progress syncs, demo reviews, and cross-team retrospectives
• Manage change control, risk tracking, and communication for multi-project programs

Quality, UAT & Continuous Optimization

• Design and coordinate UAT plans for dashboards, data pipelines, and AI agents
• Validate AI performance against qualitative (accuracy, relevance, consistency) and quantitative (latency, token cost, precision) benchmarks
• Capture feedback loops for fine-tuning models, retraining datasets, or updating prompt templates
• Ensure that delivery documentation and dashboards meet expected usability and performance standards

Strategic Enablement & Practice Building

• Define internal standards for AI project delivery, including experimentation logs, evaluation metrics, and documentation templates
• Contribute to developing multi-client AI reporting frameworks and automation playbooks
• Benchmark AI orchestration tools (LangChain, Haystack, Dust, OpenDevin) and delivery processes for continuous improvement
• Partner with leadership on roadmap prioritization, team resourcing, and delivery governance
• Mentor junior PMs and BSAs to operate effectively within AI + Data delivery contexts

Required Skills & Experience

• 6 to 10 years of relevant experience
• Proven 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 OpenAI
• Understanding of prompt engineering, context window optimization, and RAG (Retrieval-Augmented Generation) design
• Experience coordinating with teams using Python, FastAPI, SQL, and cloud platforms (AWS/Azure)
• Exposure to model monitoring, prompt versioning, and evaluation pipelines
• Ability to interpret structured/unstructured data and manage integrations between AI and data visualization layers
• Strong experience leading AI, data, or analytics product delivery using Agile/Scrum
• Skilled in backlog management, sprint planning, and multi-project coordination.
• Proficiency with PM tools — Jira, ClickUp, , Confluence, or similar.
• Excellent skills in cross-functional communication, requirements clarity, and delivery
• Certifications: PMP or Prince II
• Prior experience delivering AI agents, chatbots, copilots, or automation systems
• Exposure to AI evaluation pipelines (LangFuse, Traceloop, Arize) and prompt management tools
• Experience in marketing analytics, optimization platforms, or enterprise data systems