Technical Project Manager - SaaS/Data/GenAI Program

Roles & Responsibilities:

As a Technical Project Manager (TPM), you will own end-to-end delivery of medium to complex SaaS / Data / GenAI programs, ensuring alignment between business goals, architecture and execution.

This is not a coordination role - you are expected to bring technical depth, strong execution rigor and ownership to drive high-quality, scalable, production-ready systems.

Key Responsibilities:

• Own successful end-to-end delivery from customer discovery through production go-live and hypercare, balancing scope, quality, schedule, commercial objectives, and customer satisfaction.

• Own discovery workshops with customers and drive requirement clarification. Translate ambiguous customer requirements into clear user stories, execution plans, and aligned technical direction.

• Manage change requests throughout the project lifecycle while ensuring scope control and stakeholder alignment.

• Establish strong delivery governance, including sprint planning, tracking, release management, Definition of Ready (DoR), Definition of Done (DoD), quality gates, and continuous improvement.

• Validate estimates, capacity plans, and project schedules to ensure realistic execution plans and optimal resource utilization.

• Constantly evaluate scope vs. utilization and drive data-driven decisions to maximize delivery efficiency.

• Identify and proactively mitigate delivery, technical, resourcing, and commercial risks.

• Manage cross-team dependencies, remove blockers, and ensure smooth execution across engineering teams.

• Act as the primary customer interface, managing expectations, communication, escalations, and aligning delivery with business outcomes.

• Provide regular project and program status updates, delivery insights, and risk visibility to customers and leadership.

• Participate in architecture discussions, validate high-level design decisions, and facilitate technical trade-offs with engineering teams.

• Drive execution across backend, frontend, mobile, and data engineering teams while ensuring scalable, reliable, and production-ready solutions.

• Demonstrate a working knowledge of modern engineering practices, including microservices, event-driven architectures, CI/CD, DevOps, cloud platforms (AWS/Azure/GCP), and AI-assisted software development.

• Promote engineering excellence by encouraging observability (logging, monitoring, tracing), shift-left practices (testing, security, automation), and AI-assisted development.

• Monitor engineering and delivery metrics, including sprint predictability, release quality, production stability, defect trends, and customer satisfaction, to drive continuous improvement.