AI Project Manager

🧠 AI Project Manager – Job Description

Position: AI Project Manager

Location: Remote

Department: AI & Machine Learning

📌 Role Overview

As an AI Project Manager at Zanta.Tech, you will lead the planning, execution, and delivery of AI-driven projects. Your role is pivotal in integrating AI technologies like machine learning, natural language processing, and predictive analytics into business solutions, ensuring projects are completed on time, within scope, and aligned with strategic objectives.

🔧 Key Responsibilities

Project Planning & Execution: Develop detailed project plans, including timelines, milestones, and resource allocation. Oversee the execution of AI projects, ensuring adherence to plans and timely delivery.

Team Leadership: Assemble and lead cross-functional teams comprising data scientists, engineers, and business analysts. Foster a collaborative environment to achieve project goals.

Stakeholder Management: Communicate effectively with stakeholders to define project scope, gather requirements, and provide regular updates on project progress.

Risk Management: Identify potential risks and develop mitigation strategies to address challenges proactively.

Quality Assurance: Ensure the quality of AI models and solutions through rigorous testing and validation processes.

Ethical Oversight: Uphold ethical standards in AI development, ensuring fairness, transparency, and compliance with data privacy regulations.

🎓 Qualifications & Skills

Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

Certifications: PMP, Agile, or Scrum certifications are advantageous.

Technical Skills: Proficiency in AI/ML tools and platforms such as TensorFlow, PyTorch, AzureML, and experience with project management software like Jira or Asana.

Soft Skills: Strong leadership, communication, problem-solving, and stakeholder management abilities.

🌱 Preferred Experience

Proven experience in managing AI/ML projects from inception to completion.

Familiarity with AI project lifecycle, including data collection, model development, and deployment.

Understanding of ethical considerations in AI, including bias mitigation and data privacy.