Technical Project Manager - Data & GenAI

About Comprinno About the Role Key Responsibilities 1. End-to-End Project Ownership

• Own end-to-end delivery of Data, AI/ML, GenAI, and Agentic AI engagements.
• Translate agreed scope and business objectives into executable project plans, milestones, deliverables, and success criteria.
• Own project initiation, planning, execution, monitoring, customer acceptance, and closure.
• Build and maintain detailed delivery plans in JIRA.
• Track scope, schedule, dependencies, risks, issues, decisions, and action items.
• Ensure clear ownership and deadlines for every critical deliverable.
• Proactively identify delivery risks and drive mitigation before they impact customers.
• Manage multiple concurrent projects where required.
• Ensure projects are delivered within committed scope, schedule, and quality expectations.
• Conduct project closure, retrospectives, and lessons-learned reviews.

2. Technical Project Management

• Develop sufficient understanding of each solution to manage technical delivery effectively.
• Understand and track components including:
  • LLM-based applications
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI workflows
  • Prompt engineering
  • Data ingestion and transformation
  • Vector databases and knowledge bases
  • APIs and application integrations
  • AI/ML pipelines
  • Model evaluation
  • Cloud infrastructure
  • Security and access requirements
• Read and understand architecture diagrams, workflows, dependencies, and deployment models.
• Work with engineering leads to identify technical dependencies, constraints, and critical-path items.
• Challenge unclear estimates, dependencies, assumptions, and acceptance criteria.
• Ensure engineering decisions and scope changes are appropriately documented.

3. GenAI Delivery Lifecycle

• Ensure business use cases and expected outcomes are clearly defined before development.
• Ensure data availability, quality, access, and dependencies are identified early.
• Establish clear acceptance criteria for AI outputs.
• Coordinate evaluation and testing of GenAI solutions.
• Track accuracy, quality, latency, cost, reliability, and other relevant solution metrics.
• Ensure POCs have clearly defined success criteria and closure decisions.
• Prevent POCs from becoming indefinite experimentation exercises.
• Drive successful transition from POC to production where applicable.

4. Customer Stakeholder Management

• Act as the primary delivery point of contact for assigned customer engagements.
• Establish clear communication and governance with customers and internal stakeholders.
• Conduct:
  • Project kick-offs
  • Daily/weekly delivery reviews
  • Sprint reviews
  • Steering meetings
  • Retrospectives
  • Project closure reviews
• Communicate progress, risks, dependencies, decisions, and required customer actions transparently.
• Manage expectations around scope, timelines, dependencies, and technical constraints.
• Escalate critical risks early rather than after delivery commitments are impacted.
• Build trust through predictability, transparency, and execution discipline.

5. Agile Engineering Execution

• Facilitate sprint planning, backlog refinement, stand-ups, reviews, and retrospectives.
• Ensure user stories and technical tasks have clear ownership and acceptance criteria.
• Track sprint commitments versus actual delivery.
• Identify recurring blockers and work with engineering leadership to resolve them.
• Maintain visibility into engineering capacity and dependencies.
• Improve predictability without creating unnecessary process overhead.
• Ensure Agile practices support delivery rather than becoming administrative ceremonies.

6. Scope Change Management

• Maintain strong control over agreed project scope.
• Identify requests that constitute scope changes.
• Assess changes with engineering and business stakeholders for effort, timeline, cost, and delivery impact.
• Ensure customer expectations are reset before additional commitments are made.
• Maintain appropriate change-control documentation.
• Protect engineering teams from uncontrolled scope expansion while maintaining strong customer relationships.

7. Risk, Dependency Escalation Management

• Technical risks
• Data dependencies
• Customer dependencies
• Environment readiness
• Cloud access
• Third-party integrations
• Security approvals
• Engineering capacity
• Architecture decisions
• Scope changes

8. Reporting Governance

• Provide accurate project-health visibility to customers and Comprinno leadership.
• Maintain project dashboards and delivery metrics.
• Produce weekly and monthly reports appropriate to engagement duration.
• Track:
  • Milestone completion
  • Sprint predictability
  • Open risks/issues
  • Scope changes
  • Customer dependencies
  • Resource utilization
  • Project health
• Ensure JIRA and Confluence remain accurate sources of delivery information.
• Maintain appropriate project artifacts, decisions, meeting records, and customer approvals.

9. Quality Production Readiness

• Functional testing
• AI output/evaluation quality
• Performance
• Security
• Reliability
• Observability
• Documentation
• Deployment
• Knowledge transfer
• Customer acceptance

10. PMO Delivery Excellence

• Contribute to building Comprinnos Data GenAI delivery playbooks.
• Develop reusable templates, checklists, dashboards, and governance frameworks.
• Capture lessons from completed engagements.
• Identify recurring causes of project delays and recommend systemic improvements.
• Build reusable frameworks for GenAI POCs and production engagements.
• Share knowledge and delivery practices across the PMO organization.

Required Qualifications Skills

• Bachelors degree in Engineering, Computer Science, IT, Data Analytics, AI/ML, or a related technology discipline.
• 2-4 years of experience in technical project management, engineering delivery, or similar roles.
• Experience managing customer-facing technology projects.
• Exposure to Data Engineering, AI/ML, Generative AI, or cloud-native application projects.
• Working understanding of:
  • LLMs
  • RAG
  • Prompt Engineering
  • Agentic AI
  • Data Pipelines
  • APIs
  • Cloud Architecture
• Ability to understand architecture diagrams and technical workflows.
• Experience with Agile/Scrum delivery.
• Strong working knowledge of JIRA and project-management tooling.
• Strong project planning, estimation, risk management, and dependency-management capabilities.
• Excellent written and verbal communication.
• Strong customer and stakeholder-management skills.
• Ability to coordinate effectively across engineering, architecture, business, and customer teams.
• Strong ownership and bias for execution.

Desired Qualifications Skills

• Experience working in an AWS Partner, cloud consulting, product engineering, or technology services organization.
• Exposure to AWS AI/ML and cloud services.
• Understanding of the lifecycle from GenAI POC MVP Production.
• Exposure to AI evaluation, guardrails, responsible AI, security, and governance.
• PMP, Scrum Master, Agile, or equivalent project-management certification.
• AWS Cloud Practitioner or higher certification.
• AI/ML, GenAI, Data Engineering, or cloud certifications.
• Previous experience managing geographically distributed customers or engineering teams.

What Were Looking For

• Owns outcomes rather than meetings and trackers.
• Is technically curious and comfortable working with engineers.
• Can understand enough technology to identify when something does not make sense.
• Communicates bad news early and clearly.
• Brings structure to ambiguous AI engagements.
• Is obsessive about commitments, ownership, and follow-through.
• Can balance customer expectations with engineering realities.
• Thinks ahead about dependencies rather than reacting after they become blockers.
• Is comfortable challenging both customers and internal teams constructively.
• Learns rapidly as AI technologies evolve.

What Success Looks Like First 90 Days

• Independently manages assigned Data/GenAI engagements.
• Establishes strong delivery governance and customer communication.
• Maintains accurate project plans, RAID logs, milestones, and dependencies.
• Demonstrates sufficient technical understanding to participate meaningfully in engineering discussions.
• Proactively identifies and resolves delivery risks.

First 6 Months

• Successfully delivers multiple projects or major milestones within agreed commitments.
• Builds strong credibility with customers and engineering teams.
• Demonstrates strong control over scope, dependencies, and project risks.
• Improves project predictability and delivery visibility.
• Contributes reusable templates or practices to Comprinnos GenAI delivery framework.

First Year

• Becomes a trusted delivery owner for strategic Data GenAI engagements.
• Consistently manages multiple concurrent projects with strong customer outcomes.
• Demonstrates measurable improvement in delivery predictability and project governance.
• Contributes significantly to Comprinnos Data GenAI delivery playbooks and best practices.
• Demonstrates readiness to manage larger and increasingly complex AI transformation engagements.

Why Join Comprinno

• Work on real-world Data, GenAI, and Agentic AI implementations rather than theoretical AI initiatives.
• Participate in projects spanning Cloud, Data, AI, applications, and enterprise integrations.
• Work closely with experienced Cloud, Data, AI, and architecture teams.
• Gain exposure to the complete lifecycle from customer discovery and POC through production deployment.
• Develop rapidly in one of the fastest-evolving areas of enterprise technology.
• Build a career at the intersection of Project Management + Technology + AI + Customer Consulting.
• Work in a culture that values ownership, customer obsession, continuous learning, and execution.