Technical Project Manager

Responsibilities

• Own the planning, scheduling, execution, and delivery of high-volume data annotation projects.
• Translate model training data needs into structured project timelines and milestones.
• Implement project management methodologies (e.g., Agile, Scrum, Kanban) to track progress, unblock pipeline bottlenecks, and manage backlogs.
• Forecast resource needs, track operational budgets, and align data deliveries with the release cycles of the Data Science and ML Engineering teams.
• Proactively identify project risks (such as edge cases, delivery delays, or drop-offs in accuracy) and establish contingency plans.
• Serve as the primary point of contact for external Business Process Outsourcing (BPO) partners, labeling vendors, and global crowd-sourced workforces.
• Define, monitor, and enforce service-level agreements (SLAs) and key performance indicators (KPIs) regarding vendor throughput, speed, and cost efficiency.
• Lead the selection, negotiation, and onboarding processes for new data vendors to strategically scale operational capacity as company needs evolve.
• Collaborate with engineering teams to build and refine airtight, unambiguous annotation guidelines, taxonomies, and training materials.
• Design and enforce rigorous data auditing processes, measuring metrics like Inter-Annotator Agreement (IAA), consensus scores, and precision/recall of human labels.
• Evaluate and manage data labeling tools and platforms (e.g., Labelbox, Scale AI, CVAT, or custom internal interfaces) to maximize annotator efficiency.

Requirements

• Experience: 4+ years of professional experience in Data Operations, Technical Project Management, or MLOps, with a heavy emphasis on handling AI/ML training data.
• Project Management Skills: Demonstrated track record of managing complex, cross-functional projects from inception to completion.
• Data Annotation Background: Hands-on experience designing and executing text, image, video, audio, or multimodal data labeling workflows.
• Vendor Management: Experience directly managing, communicating with, and auditing third-party vendors or offshore operations.
• Analytical Capabilities: Strong data tracking habits with the ability to build performance dashboards and parse metrics via Excel/Google Sheets or SQL.
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
• Nice to have: Experience with advanced labeling workflows, such as RLHF (Reinforcement Learning from Human Feedback), red-teaming, or prompt-response evaluation for Large Language Models (LLMs).
• Basic understanding of Python or automation scripts to seamlessly move data across different systems and APIs.
• Experience working in a fast-paced, high-growth environment where workflows iterate rapidly.

About the role

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.