Project Lead

About pi-labs

pi-labs is a deep technology company building next-generation platforms for digital forensics, investigations, and intelligence. We combine advances in Artificial Intelligence, Computer Vision, Multimodal Intelligence, and distributed systems to solve some of the most complex challenges in analyzing and correlating information at scale.

Our products transform vast volumes of structured and unstructured data into actionable intelligence through multimodal AI, intelligent document processing, facial recognition, multimedia analytics, entity resolution, graph analytics, and semantic search. Designed for mission-critical environments. 

Innovation is at the core of everything we build. Our engineering teams work on large-scale AI models, high-performance computing, GPU acceleration, distributed data systems, and advanced analytics to deliver solutions that operate reliably under demanding real-world conditions.

At pi-labs, you'll work alongside engineers, AI researchers, and domain specialists who are passionate about translating cutting-edge research into production-grade products. If you enjoy tackling technically challenging problems and building technologies that redefine the future of digital investigations and intelligence, pi-labs is the place to make a meaningful impact.

Experience: 6 -10 years 

Location: Pune, India / Travel to Customer Site / Hybrid, based on project needs 

Role Purpose 

Own end-to-end delivery and deployments for customer implementations, including planning, technical coordination, data onboarding, integration, deployment readiness, customer engagement, and post-go-live stabilization. The role requires strong Python and data engineering expertise combined with team leadership and customer-facing delivery ownership. 

Key Responsibilities 

• Lead end-to-end implementation and deployment of solutions across customer environments, including edge, on-premises, private cloud, and enterprise deployments. 
• Own delivery plans, milestones, dependencies, risks, issues, acceptance criteria, and go-live readiness. 
• Coordinate Python, data engineering, AI/LLM, DevOps, UI, QA, and integration teams to ensure timely execution. 
• Translate customer requirements into implementation workstreams, technical tasks, configuration requirements, and delivery priorities. 
• Lead data onboarding activities including source assessment, data mapping, ingestion readiness, schema validation, transformation requirements, and data-quality checks. 
• Provide technical guidance on Python services, APIs, ETL/ELT pipelines, data processing, workflow orchestration, and integration patterns. 
• Drive deployment planning across infrastructure provisioning, environment configuration, access controls, security prerequisites, release management, and production validation. 
• Act as the primary customer-facing delivery lead for implementation discussions, technical workshops, status reviews, issue resolution, and operational handover. 
• Manage customer expectations, communicate delivery status clearly, and escalate risks or dependencies proactively. 
• Lead onsite or remote deployment activities, including installation, configuration, user acceptance testing, training coordination, and support. 
• Review delivery quality, validate technical outputs, and ensure that implemented features meet agreed functional and operational requirements. 
• Establish implementation documentation including deployment runbooks, integration specifications, configuration guides, UAT checklists, release notes, and handover documents. 
• Mentor implementation engineers and ensure appropriate task allocation, technical review, accountability, and delivery discipline across the project team. 
• Support pre-sales and solutioning teams when required for implementation estimation, deployment architecture, technical feasibility, and customer workshops. 

Required Technical Skills 

• Strong hands-on experience in Python development for APIs, automation, backend services, data processing, or integration services. 
• Strong understanding of data engineering concepts including ETL/ELT pipelines, data validation, transformation, orchestration, data modelling, and ingestion frameworks. 
• Experience working with databases such as PostgreSQL, ClickHouse, Elasticsearch/OpenSearch, MongoDB, Cassandra, or similar platforms. 
• Exposure to API integration, REST services, message queues, event-driven processing, and enterprise system integration. 
• Understanding of Linux environments, Docker, Kubernetes, CI/CD pipelines, deployment automation, and environment management. 
• Familiarity with cloud, on-premises, edge deployment, or air-gapped deployment environments. 
• Experience with data platforms, analytics systems, Knowledge Graphs, AI/LLM platforms, or investigation/intelligence platforms is an advantage. 
• Ability to review logs, troubleshoot deployment issues, identify root causes, and coordinate resolution across engineering teams. 

Leadership and Delivery Skills 

• Demonstrated experience leading cross-functional technical teams. 
• Strong ownership mindset with the ability to independently drive projects from kick-off to production handover. 
• Ability to manage multiple workstreams, customer dependencies, technical risks, and changing priorities. 
• Good understanding of Agile delivery, sprint planning, backlog management, release planning, and UAT coordination. 
• Ability to create structured project plans, implementation trackers, issue logs, risk registers, and executive status reports. 
• Strong decision-making capability under delivery pressure. 

Customer Handling Skills 

• Excellent verbal and written communication skills. 
• Comfortable leading customer workshops, implementation discussions, technical walkthroughs, and governance meetings. 
• Ability to explain technical concepts clearly to business users, customer IT teams, leadership stakeholders, and implementation partners. 
• Strong stakeholder management and conflict-resolution capability. 
• Ability to manage customer expectations while protecting delivery timelines, scope, and quality. 

Qualification 

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or related discipline. 
• Certifications in Project Management, Agile, Scrum, Cloud, DevOps, or Data Engineering are preferred but not mandatory. 

Preferred Experience 

• Experience in enterprise product implementation, SaaS deployment, digital transformation, analytics platforms, intelligence platforms, or data platform deployments. 
• Experience handling government, enterprise, defence, law-enforcement, telecom, BFSI, or regulated customer deployments is an advantage. 
• Experience managing distributed teams and customer-site implementation teams is preferred.