Technical Project Manager

Role Overview

The Technical Project Manager (TPM) orchestrates complex, multi-stakeholder initiatives across research, development, deployment, and ecosystem partnerships. You’ll bridge technical teams, academia, government partners, and commercial stakeholders—ensuring projects deliver on scope, timeline, and quality while proactively identifying risks and driving continuous improvement.

Key Responsibilities

• Project Planning & Execution - Define scope, objectives, milestones, and success metrics with technical leads - Develop comprehensive project plans, timelines, and resource allocation strategies - Establish clear deliverables, dependencies, and critical path items.
• Project Monitoring & Control - Set up real-time dashboards and KPI tracking using project management tools (Jira, Asana) - Monitor project health: budget, timeline, resources, scope, quality - Generate status reports with variance analysis and trend identification - Track blockers, dependencies, and risks with proactive resolution.
• Risk & Issue Management - Identify potential risks early through stakeholder engagement - Maintain risk registers and develop mitigation/contingency plans - Escalate critical issues with context, severity, and recommended solutions - Drive continuous improvement through retrospectives.
• Stakeholder Coordination - Act as single point of contact for cross-functional teams and external partners - Facilitate collaboration between researchers, engineers, DevOps, data teams, and business units - Manage competing priorities and negotiate resource allocation - Maintain transparency through consistent communication.
• Gap Identification & Process Improvement - Conduct process audits to identify inefficiencies and bottlenecks - Analyze root causes of delays; recommend improvements - Identify resource gaps (skills, tools, infrastructure) - Drive adoption of best practices across teams
• Dashboard & Reporting - Design and build executive dashboards for project visibility - Create automated reporting systems (burn-down charts, velocity reports, forecasts) - Develop data-driven recommendations for stakeholders - Customize reporting by audience (technical teams vs. leadership).

Required Qualifications

• Experience - 7+ years of project management experience, with 4+ years managing technical/R&D projects, with engineering background from any discipline, CSE preferred - Proven track record with multi-stakeholder initiatives - Agile/Waterfall/hybrid methodology expertise (PMP, CSM, or PRINCE2 certification preferred) - Proficiency with Jira, Asana, Monday.com, or MS Project - Cross-functional team management (engineers, researchers, external partners), Preferably 2 years of experience in AI/ML domain.
• Technical Skills - Working knowledge of software development lifecycle (SDLC) - Expertise in AI domain. Understanding of AI/ML project workflows and research timelines Ability to translate technical requirements into project plans - Data analysis tools: Excel, Google Sheets, Tableau, Power BI - Comfortable with command-line tools, APIs, Git basics. Must have hands-on coding experience.
• Soft Skills - Excellent communication: articulate complex concepts to diverse audiences - Proactive problem-solving: identify issues early and escalate with solutions - Stakeholder management: navigate competing priorities and build consensus - Attention to detail: maintain documentation and compliance standards - Adaptability: thrive in fast-paced, evolving research environment - Leadership presence: drive accountability without formal authority
• Preferred Domain Knowledge - Generative AI, LLMs, or ML model development experience - Indian language NLP or multilingual AI projects - Government-funded or public-private partnership experience - Understanding of academic research workflows

Typical Project Types You’ll Manage

• Model Development: Foundation model training (LLMs, ASR, TTS) across Indian languages
• Infrastructure & Deployment: CI/CD pipelines, monitoring systems, production deployments
• Research & Experimentation: Structured experiments with clear hypotheses and measurement plans
• Data Initiatives: Bharat Data Sagar expansion, annotation, quality assurance, versioning
• Ecosystem Partnerships: Collaborations with startups, government, and industry
• Upskilling Programs: Hackathons, courses, internships, researcher onboarding

Work Location: In person