Technical Project Manager (Data & AI Practices)

• TPM

Technical Project Manager (Data & AI Practices)

Role: Technical Project Manager (TPM) Department: Delivery / Consulting Services

Reporting To: VP Delivery Location: Noida Role Summary

We are looking for a Technical Project Manager who serves as the operational anchor for our Data Engineering and AI/ML engagements. In this role, you will bridge the gap between complex technical implementation (Data Pipelines, MLOps, Analytics) and client business objectives.

You will lead cross-functional squads of Data Engineers, Data Scientists, and ML Engineers. Your mandate is to deliver high-quality solutions while navigating the nuances of service-based engagement models, including Time & Material (T&M) and Build-Operate-Transfer (BOT). You must be fluent in Agile methodologies and possess deep technical literacy in modern data architectures.

Key Responsibilities 1. Technical Delivery & Lifecycle Management

- Orchestrate Data & AI Projects: Manage the end-to-end lifecycle of data projects, from raw data ingestion (ELT/ETL) to AI Model deployment and monitoring.

- Technical Literacy: actively participate in architectural discussions. You must understand the implications of technical choices (e.g., Batch vs. Real-time processing, Cloud costs of GPU clusters) to make informed planning decisions.

- Practice Alignment: Collaborate with Practice Leadership (AI/ML, Data Eng) to ensure your team adheres to the organizations coding standards, security protocols, and architectural best practices.

- Ambiguity Management: Specifically for AI/ML, manage the uncertainty of R&D phases (e.g., when a model fails to converge) and translate that into predictable timelines for the client.

2. Client Engagement & Consulting

- Expectation Management: Act as the primary liaison between the technical team and the client stakeholders. Translate "Technical Debt" and "Model Drift" into business risks and requirements.

- Scope Governance: rigorously manage scope creep. Identify when client requests deviate from the SOW and handle Change Requests (CRs) commercially.

- Client Education: Guide clients who are new to AI/Data on the prerequisites of the project (e.g., Data Quality, Access Rights, Infrastructure needs).

3. Operational Excellence (Engagement Models)

- For T&M Engagements: Monitor team utilization, burn rates, and velocity. Ensure the client sees visible value in every sprint to justify the ongoing investment.

- For BOT (Build-Operate-Transfer):

o Build: Ensure documentation is created during development, not after.

o Operate: Establish SLAs and Runbooks for the solution.

o Transfer: Lead the knowledge transfer sessions to upskill the client s internal team for the eventual handover.

4. Agile Project Management

- Run Scrum ceremonies (Stand-ups, Planning, Retro, Demos) tailored for Data/AI teams.

- Maintain the backlog in Jira/Azure DevOps, ensuring User Stories meet the "Definition of Ready" (DoR) regarding data availability before development starts.

- Remove impediments, specifically those unique to data (e.g., "Waiting for firewall access to Source Database")

Required Skills & Qualifications Technical Knowledge (Must Have)

- Data Engineering: Understanding of Modern Data Stack (Snowflake, Databricks, BigQuery, Redshift), ETL/ELT tools (dbt, Airflow, ADF), and Data Modeling concepts.

- AI/ML Awareness: Understanding of the Data Science lifecycle (Training, Testing, Validation) and MLOps concepts (Model Registry, Drift Monitoring).

- Cloud Native: Familiarity with AWS, Azure, or GCP ecosystems.

- Coding Literacy: Ability to read/understand SQL and Python (You wont code daily, but you must understand what your team is building).

Project Management Competencies

- Methodology: Certified Scrum Master (CSM), SAFe Agilist, or PMP.

- Experience: 5+ years in Project Management with at least 3 years focused on Data, BI, or Analytics projects.

- Financials: Experience managing project P&L, resource forecasting, and margin analysis.

Soft Skills

- Structured Thinking: Ability to break down complex abstract data problems into executable tasks.

- Diplomacy: Experience navigating matrix organizations and managing difficult client conversations regarding timelines and budget.