Technical Project Manager (Data & AI Practices)

Quarks Technosoft Pvt. Ltd.; Dadri; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Quarks Technosoft Pvt. Ltd. in Noida is seeking a Technical Project Manager to anchor Data & AI engagements, bridging data engineering, ML, and business objectives.

Lead cross-functional squads of data engineers, scientists, and ML engineers to deliver high-quality solutions in T&M and BOT models. You will drive agile ceremonies, manage the lifecycle, and align with practice leadership to uphold coding standards, security, and architecture best practices while handling AI/ML R&D uncertainties

Qualifications

• Understanding of Modern Data Stack including Snowflake, Databricks, BigQuery and Redshift.
• Familiar with ETL/ELT tools (dbt, Airflow, ADF) and data modeling concepts.
• Awareness of Data Science lifecycle and MLOps concepts.

Responsibilities

• Orchestrate end-to-end Data & AI projects from ingestion to model deployment and monitoring.
• Participate in architectural discussions; understand implications of tech choices for cost and performance.
• Collaborate with Practice Leadership to ensure coding standards, security, and best practices.
• Manage AI/ML initiatives with clear timelines amid R&D uncertainty and client expectations.

Skills

Snowflake

Databricks

BigQuery

Redshift

ETL/ELT tools

Data Modeling concepts

Data Science lifecycle

MLOps concepts

Cloud platforms (AWS/Azure/GCP)

SQL

Python

CSM

SAFe Agilist

PMP

Project P&L

Resource forecasting

Automation & DevOps

Tools

Jira

Azure DevOps

Git

Job description

Job Description: Technical Project Manager (Data & AI Practices)

12-18 Years Noida

• TPM

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

Reporting To: VP Delivery Location: Noida Role Summary

We are looking for a Technical Project Managerwho serves as the operational anchor for our Data Engineering and AI/MLengagements. In this role, you will bridge the gap between complex technicalimplementation (Data Pipelines, MLOps, Analytics) and client businessobjectives.

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

KeyResponsibilities 1. Technical Delivery & Lifecycle Management

• OrchestrateData & AI Projects: Manage the end-to-end lifecycle of data projects,from raw data ingestion (ELT/ETL) to AI Model deployment and monitoring.
• TechnicalLiteracy: actively participate in architectural discussions. You mustunderstand the implications of technical choices (e.g., Batch vs. Real-time processing, Cloudcosts of GPU clusters) to make informed planning decisions.
• PracticeAlignment: Collaborate with PracticeLeadership (AI/ML, Data Eng) to ensure your team adheres to theorganization's coding standards, security protocols, and architectural bestpractices.
• AmbiguityManagement: Specifically for AI/ML, manage the uncertainty of R&Dphases (e.g., when a model fails to converge) and translate that intopredictable timelines for the client.

2. Client Engagement & Consulting

• ExpectationManagement: Act as the primary liaison between the technical team and theclient stakeholders. Translate "Technical Debt" and "ModelDrift" into business risks and requirements.
• ScopeGovernance: rigorously manage scope creep. Identify when client requestsdeviate from the SOW and handle Change Requests (CRs) commercially.
• ClientEducation: Guide clients who are new to AI/Data on the prerequisites of theproject (e.g., Data Quality, Access Rights, Infrastructure needs).

3. OperationalExcellence (Engagement Models)

• ForT&M Engagements: Monitor team utilization, burn rates, and velocity.Ensure the client sees visible value in every sprint to justify the ongoinginvestment.
• Build:Ensure documentation is created duringdevelopment, not after.
• Operate:Establish SLAs and Runbooks for the solution.
• Transfer:Lead the knowledge transfer sessions to upskill the client’s internal team forthe eventual handover.

4. AgileProject 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) regardingdata availability before development starts.
• Remove impediments, specifically those unique todata (e.g., "Waiting for firewall access to Source Database")

Required Skills& Qualifications Technical Knowledge (Must Have)

• DataEngineering: Understanding of Modern Data Stack (Snowflake, Databricks,BigQuery, Redshift), ETL/ELT tools (dbt, Airflow, ADF), and Data Modelingconcepts.
• AI/MLAwareness: Understanding of the Data Science lifecycle (Training, Testing,Validation) and MLOps concepts (Model Registry, Drift Monitoring).
• CloudNative: Familiarity with AWS, Azure, or GCP ecosystems.
• CodingLiteracy: Ability to read/understand SQL and Python (You won't code daily,but you must understand what your team is building).
• Methodology:Certified Scrum Master (CSM), SAFe Agilist, or PMP.
• Experience:5+ years in Project Management with at least 3 years focused on Data, BI, orAnalytics projects.
• Financials:Experience managing project P&L, resource forecasting, and margin analysis.

Soft Skills

• StructuredThinking: Ability to break down complex abstract data problems intoexecutable tasks.
• Diplomacy:Experience navigating matrix organizations and managing difficult clientconversations regarding timelines and budget.