Technical Lead/ Project Lead

Role Summary:

The Technical Product / Project Lead (TPL) will be a key member of K force Consulting Solutions (KCS) - AI CoE team, responsible for managing the execution of AI and automation initiatives from intake through delivery.

This individual will coordinate between the U.S. AI CoE leadership, practice SMEs (Cloud, Digital, App Engineering, Data), and KCS AI CoE development team, ensuring projects are delivered with quality, speed, and alignment to business outcomes. They will play a dual role — translating business requirements into actionable technical deliverables and providing hands-on delivery oversight for pilots and scaled implementations.

Key Responsibilities

• Program & Delivery Ownership Own end-to-end delivery for AI/automation projects assigned to the KCS AI CoE team — from backlog intake through pilot and production rollout.
• Drive sprint planning, backlog refinement, and milestone tracking in Azure DevOps (ADO).
• Maintain visibility into task status, risks, and dependencies across the offshore development team.
• Ensure delivery adherence to AI CoE governance, security, and compliance standards.

Technical & Product Translation

• Translate business requirements (captured via intake and AI CoE validation) into technical scope, user stories, and acceptance criteria.
• Partner with data scientists, engineers, and SMEs to define architecture, data sources, and integration needs.
• Review functional and technical designs to ensure they align with KCS AI CoE standards and client needs.

Stakeholder & Communication Management

• Serve as the day-to-day liaison between U.S. AI CoE, Steering Committee, and IDC teams.
• Communicate progress, risks, and blockers through dashboards and structured updates.
• Foster trust and transparency with practice leads and client-facing delivery teams.

Process & Quality Governance

• Ensure every initiative follows the standard KCS AI CoE process (Intake > Prioritization > Pilot > Scale > Monitoring).
• Maintain documentation, including Business Requirement Summaries, Technical Design Docs, and Pilot Reports.
• Conduct quality checks on backlog readiness, sprint output, and release deliverables.

People & Collaboration

• Coordinate cross-functional collaboration between developers, data engineers, ML engineers, and QA.
• Support onboarding and knowledge sharing as new team members join the AI CoE team.
• Drive a culture of accountability, continuous improvement, and delivery excellence.

Required Skills & Experience

Technical Skills

• 8–12 years of experience managing technology or AI/automation projects (preferably with hybrid onshore-offshore models).
• Proven experience with Azure DevOps (ADO) or Jira for backlog and sprint management.
• Working knowledge of AI/ML lifecycle (data prep, model training, validation, deployment, monitoring).
• Familiarity with Azure AI services, OpenAI APIs, and cloud integration concepts. Basic understanding of data engineering pipelines (ETL, SQL, Python) and application integration frameworks.
• Strong grasp of governance, compliance, and risk management principles for AI projects.

Product & Delivery Skills

• Ability to translate business use cases into structured technical requirements.
• Experience defining KPIs, success criteria, and ROI metrics for pilots and scale solutions.
• Excellent backlog grooming, prioritization, and dependency management skills.
• Strong communication and stakeholder management with U.S.-based leadership teams.

Soft Skills

• Self-driven, structured, and detail-oriented with a delivery-first mindset.
• Exceptional written and verbal communication skills.
• Proven ability to manage ambiguity and drive clarity in complex environments.
• Comfortable leading daily stand-ups, retrospectives, and technical deep dives.

Preferred Qualifications

• PMP, CSM, or SAFe certification preferred.
• Experience working in or building an AI Centre of Excellence or data-driven product organization.
• Prior experience with U.S. clients in consulting or managed service environments.

Success Indicators

• Projects delivered on time, within scope, and aligned with defined KCS AI CoE outcomes.
• Clear traceability from intake through execution in ADO.
• Positive feedback from U.S. CoE and practice leaders on communication and delivery quality.
• Reusable project documentation and accelerators created for future AI initiatives.

Job Type: Full-time

Pay: Up to ₹3,800,000.00 per year

Benefits:

• Leave encashment
• Paid sick time
• Paid time off
• Provident Fund

Ability to commute/relocate:

• Pune, Maharashtra: Reliably commute or planning to relocate before starting work (Required)

Experience:

• Azure DevOps: 8 years (Required)

Work Location: In person