Design scalable AI/ML workloads aligned with organizational goals

Develop and maintain reproducible Machine Learning pipelines

Deploy AI models to production using model serving infrastructure

Implement observability frameworks for monitoring and logging AI services

Define infrastructure requirements for MLOps pipelines and components

Collaborate with infrastructure engineers to support implementation of infrastructure

Mentor and coach team members to promote best practices and continuous improvement

Coordinate with cross-functional teams including data scientists and engineers

Optimize ML workloads for performance and scalability

Ensure compliance with security and data privacy standards

Evaluate new tools and technologies to enhance AI service delivery

Document system architectures and processes for knowledge sharing

Troubleshoot and resolve production issues related to AI services

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field

3+ years of experience in AI, Machine Learning, data engineering, software development, or cloud infrastructure

Strong proficiency in Python and experience with AI/ML frameworks such as PyTorch, TensorFlow, HuggingFace, or Scikit-learn

Experience with model inference runtimes like vLLM, MLServe, or Torch Serve

Proficiency in containerization and orchestration tools including Docker and Kubernetes

Experience defining and implementing infrastructure requirements for ML pipelines

Strong problem-solving skills and ability to work in agile, cross-functional teams

Good communication and mentoring skills to support team development

B2+ level English proficiency

Experience with cloud platforms such as Azure, AWS, or Google Cloud

Knowledge of Infrastructure as Code (IaC) practices

Familiarity with experiment tracking systems and pipeline orchestration tools