Design scalable AI and machine learning workloads that align with company objectives

Develop and uphold reproducible machine learning pipelines

Deploy AI models into production using model serving infrastructures

Implement monitoring and logging frameworks for AI service observability

Define infrastructure needs for MLOps pipelines and related components

Collaborate with infrastructure engineers to facilitate infrastructure deployment

Guide and mentor team members to encourage best practices and ongoing improvement

Coordinate efforts with cross-functional teams including data scientists and engineers

Optimize machine learning workloads for enhanced performance and scalability

Ensure adherence to security protocols and data privacy regulations

Assess new tools and technologies to improve AI service delivery

Document system designs and workflows for knowledge dissemination

Diagnose and resolve production issues affecting AI services

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

Over 5 years of experience in AI, machine learning, data engineering, software development, or cloud infrastructure

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

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

Proficiency in containerization and orchestration technologies such as Docker and Kubernetes

Experience specifying and implementing infrastructure requirements for ML pipelines

Strong analytical and problem-solving capabilities with experience in agile cross-disciplinary teams

Effective communication and mentoring abilities to support team growth

English language proficiency at B2 level or higher

Familiarity with cloud platforms like Azure, AWS, or Google Cloud

Understanding of Infrastructure as Code (IaC) methodologies

Experience with experiment tracking systems and pipeline orchestration tools