Project Manager-Data Annotator(NLP)

Job Title - Project Manager

Years of Experience - 4+Years

Location - Karur&Coimbatore

Notice Period - Immediate to 30 Days

Mandatory: Hands-on experience in NLP projects (Data Annotation)

Job Summary

We are looking for an experienced Project Manager to oversee Data Annotation, Data Labeling, and AI Data Operations projects. The candidate will be responsible for project planning, resource allocation, client communication, quality management, and successful delivery of datasets for Machine Learning and Generative AI applications.

Key Responsibilities

• Manage end-to-end Data Annotation and AI Data Operations projects.
• Define project scope, timelines, milestones, and deliverables.
• Coordinate with clients, stakeholders, annotation teams, and ML teams.
• Develop project plans and resource allocation strategies.
• Track project progress and ensure on-time delivery.
• Monitor project risks and implement mitigation plans.
• Ensure data quality standards and client requirements are met.
• Conduct project status meetings and stakeholder reviews.
• Manage budgets, utilization, and productivity metrics.
• Oversee quality assurance and process improvement initiatives.
• Prepare project reports, dashboards, and executive summaries.
• Support scaling of annotation operations for large datasets.

Required Skills

The ideal candidate should possess a strong blend of project management expertise, operational leadership capabilities, and domain knowledge in AI/ML data operations. The role requires the ability to manage complex projects, coordinate cross-functional teams, ensure data quality, and successfully deliver AI-ready datasets that meet client expectations.

Project Management & Operational Excellence

• Proven experience in project planning, execution, monitoring, and successful delivery of multiple projects within defined timelines and budgets.
• Strong capability in resource planning, workload balancing, and capacity management to optimize team productivity and operational efficiency.
• Expertise in identifying project risks, dependencies, and bottlenecks, along with developing effective mitigation and contingency plans.
• Experience managing project scope, timelines, deliverables, and stakeholder expectations throughout the project lifecycle.
• Strong understanding of SLA management, KPI tracking, performance reporting, and continuous process improvement.
• Ability to drive operational excellence through process standardization, workflow optimization, and quality-focused execution.
• Experience in change management, including handling evolving client requirements, process enhancements, and organizational priorities.

Client & Stakeholder Management

• Excellent stakeholder management skills with the ability to collaborate effectively with clients, business leaders, operations teams, quality teams, and AI/ML stakeholders.
• Proven experience in managing client communications, conducting project reviews, providing status updates, and ensuring customer satisfaction.
• Ability to translate business requirements into actionable project plans and operational strategies.
• Strong relationship-building skills with a customer-centric approach to project delivery.

AI/ML & Data Operations Knowledge

• Strong understanding of the end-to-end Data Annotation and Data Labeling lifecycle across text, image, audio, video, and multimodal datasets.
• Knowledge of annotation methodologies, labeling strategies, and quality assurance processes used in Machine Learning and Artificial Intelligence projects.
• Familiarity with dataset preparation, validation, and curation requirements for Machine Learning, Deep Learning, and Generative AI applications.
• Understanding of Reinforcement Learning from Human Feedback (RLHF), Large Language Model (LLM) evaluation, prompt-response assessment, and AI model training workflows.
• Experience working with AI data quality frameworks, validation methodologies, accuracy measurement techniques, and continuous quality improvement processes.
• Ability to collaborate with Data Scientists, Machine Learning Engineers, and AI teams to ensure datasets align with model training objectives and business requirements.

Quality & Performance Management

• Strong focus on data quality, accuracy, consistency, and compliance with client-specific guidelines and standards.
• Experience establishing quality metrics, validation frameworks, audit mechanisms, and performance benchmarks.
• Ability to analyze productivity, throughput, quality trends, a