About Comprinno

Comprinno is a NASSCOM-incubated technology company headquartered in Bangalore, with offices in Pune, Coimbatore, and the United States. We help organizations accelerate digital transformation through Cloud Migration & Modernization, DevOps & Platform Engineering, Managed Services, Security & Compliance, Data Analytics, and Artificial Intelligence.
As a leading AWS consulting partner, Comprinno works with startups, digital-native businesses, and enterprises to build scalable, secure, resilient, and high-performing technology platforms.
Our flagship SaaS platform, Tevico, provides intelligent CloudOps, FinOps, SecOps, governance, compliance, observability, and automation capabilities, helping organizations operate their cloud environments more efficiently.
With a growing Data & AI practice, Comprinno is building enterprise solutions across Generative AI, Agentic AI, Data Engineering, Machine Learning, intelligent automation, and AI-powered applications.

About the Role

We are looking for a technically grounded and execution-focused Technical Project Manager – Data & GenAI to manage customer-facing Data, AI/ML, Generative AI, and Agentic AI engagements.
This is not a project-coordination-only role.
The Technical Project Manager will be accountable for taking engagements from requirements and planning through engineering execution, customer acceptance, and successful closure.
You will work closely with customers, Data Engineers, AI/ML Engineers, GenAI Engineers, Cloud Architects, Solution Architects, and other engineering teams to convert business objectives into executable delivery plans and drive those plans to completion.
The ideal candidate does not need to be the deepest AI engineer in the room but must possess sufficient technical depth to understand architectures, ask the right questions, identify dependencies and risks, challenge unrealistic assumptions, and communicate technical progress credibly to customers.

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.
The TPM is not expected to replace the Solution Architect or Engineering Lead, but must be technically capable of managing their execution.

3. GenAI Delivery Lifecycle

Drive projects through a structured lifecycle such as:
Discovery → Requirements → Architecture → Data Readiness → Build → Evaluate → Test → Deploy → Customer Acceptance → Production Handover
  • 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

Maintain active visibility into:
  • Technical risks
  • Data dependencies
  • Customer dependencies
  • Environment readiness
  • Cloud access
  • Third-party integrations
  • Security approvals
  • Engineering capacity
  • Architecture decisions
  • Scope changes
Maintain RAID logs where appropriate and drive risks to resolution.
Escalate issues with context, business impact, options, owner, and recommended action, rather than merely reporting problems.

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

Work with engineering teams to ensure solutions are not considered complete merely because development has finished.
Track readiness across:
  • Functional testing
  • AI output/evaluation quality
  • Performance
  • Security
  • Reliability
  • Observability
  • Documentation
  • Deployment
  • Knowledge transfer
  • Customer acceptance
Ensure clear handover into production support or Managed Services where applicable.

10. PMO & Delivery Excellence

  • Contribute to building Comprinno's 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

  • Bachelor's 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 We're Looking For

We are looking for someone who:
  • 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 Comprinno's 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 Comprinno's 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.
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