Description

You will lead the end-to-end machine learning lifecycle and manage a team of data scientists and engineers to deliver AI solutions.

Responsibilities

• Lead the end-to-end ML project lifecycle from data exploration and model development to deployment and maintenance.
• Manage and mentor a team of data scientists and ML engineers, providing technical direction and performance feedback.
• Collaborate with sales and business development to identify client needs, formulate AI/ML solutions, and present technical proposals.
• Drive the strategic roadmap for AI initiatives and establish best practices for solution design, code reviews, and model validation.
• Implement MLOps best practices including CI/CD, containerization, and model versioning.

Required Skills

• 12+ years of industry experience in data science or ML engineering, with 5+ years in a leadership or management role.
• Expertise in classical machine learning, including regression, clustering, decision trees, and ensemble methods.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Practical experience with generative AI, including GANs, VAEs, or transformer-based architectures.
• Proficiency in MLOps tools and containerization, specifically Docker and Kubernetes.
• Experience managing cloud infrastructure across AWS, Azure, or GCP.
• Strong background in CI/CD pipelines and automation.
• Ability to translate complex requirements into actionable project scopes and technical estimates.
• Master's degree in Computer Science, Data Science, Engineering, or a related field.

Preferred Skills

• Experience with big data ecosystems like Spark or Hadoop.
• Knowledge of DevOps tools such as Jenkins, GitLab CI, or Terraform.
• Background in NLP, computer vision, or recommendation systems.

Key Skills

Education

Master's degree