Technical Project Manager - GenAI/SaaS

Role Overview:

- We are looking for a highly driven Technical Project Manager (TPM) to lead end-to-end delivery of complex SaaS, AI/GenAI, and Data Engineering programs for global customers.

- This is a highly technical, execution-focused role requiring strong ownership, engineering understanding, delivery management, and stakeholder communication skills.

- The ideal candidate should be comfortable working closely with engineering teams, participating in architecture discussions, and driving high-quality execution across distributed teams.

Key Responsibilities:

- Own end-to-end delivery of SaaS, AI/GenAI, and Data Engineering programs

- Translate business requirements into clear execution plans and technical deliverables

- Drive Agile delivery, sprint planning, estimation, release management, and execution governance

- Collaborate with backend, frontend, cloud, and data engineering teams

- Participate in architecture discussions and technical decision-making

- Ensure scalable, secure, reliable, and production-ready system delivery

Drive modern engineering practices including:

- Microservices

- Event-driven systems

- CI/CD

- DevOps

- Cloud-native architecture

- Manage customer communication, delivery updates, risks, dependencies, and escalations

- Track engineering KPIs including velocity, predictability, defects, and delivery metrics

- Drive continuous improvement, automation, and operational efficiency

- Mentor teams on Agile practices, execution discipline, and engineering best practices

Required Skills & Experience:

- 8+ years of experience in Technical Program/Project Management or Engineering Delivery

Strong understanding of:

- Backend systems

- APIs

- Microservices architecture

- Distributed systems

Working knowledge of:

- Node.js backend ecosystem

- React/frontend architecture

- Cloud platforms (AWS/Azure/GCP)

- CI/CD and DevOps practices

- Experience working with global customers in client-facing roles

- Strong exposure to Agile/Scrum methodologies

- Ability to participate in technical discussions and architecture reviews

- Strong communication, stakeholder management, and problem-solving skills

- High ownership mindset and ability to manage multiple workstreams

Good to Have:

Hands-on engineering background in:

- Java

- Python

- Golang

- Rust

Exposure to:

- Databricks

- Kafka

- Data Engineering platforms

- GenAI / LLM systems

- RAG architectures

Understanding of:

- Monitoring & observability

- Performance optimization

- Reliability engineering

- Cloud cost optimization