Design scalable AI and ML workloads that align with company objectives

Develop and sustain reproducible machine learning pipelines

Deploy AI models into production utilizing model serving infrastructures

Implement monitoring and logging frameworks for AI services observability

Define infrastructure needs for MLOps pipelines and related components

Collaborate with infrastructure engineers to facilitate infrastructure deployment

Mentor and guide team members to encourage best practices and ongoing improvement

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

Optimize ML workloads to enhance 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 to support knowledge sharing

Troubleshoot and resolve AI service production issues

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

Minimum 7 years’ experience in AI, machine learning, data engineering, software development, or cloud infrastructure

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

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

Proficiency with containerization and orchestration tools including Docker and Kubernetes

Experience specifying and implementing infrastructure requirements for ML pipelines

Strong analytical and problem-solving skills with ability to operate within agile, cross-functional teams

Effective communication and mentoring abilities to support team growth

English language proficiency at B2 level or higher

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

Understanding of Infrastructure as Code (IaC) methodologies

Familiarity with experiment tracking tools and pipeline orchestration systems