Machine Learning Engineer

Details

• Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications.
• Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models.
• Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices.
• Build and maintain workflow orchestration, feature engineering, and data processing pipelines.
• Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health.
• Manage the end-to-end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability.
• Containerize ML workloads with Docker and deploy scalable services using cloud-native technologies and orchestration platforms.
• Develop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources.
• Collaborate with Data Scientists and software engineers to productionize, optimize, and scale machine learning solutions.
• Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.
• Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, or a related field.
• Strong Python programming skills and proficiency with SQL.
• Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management.
• Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker.
• Strong understanding of the end-to-end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance.
• Experience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation).
• Experience with Docker, containerized ML workloads, and container orchestration platforms such as Kubernetes.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability.
• Familiarity with feature stores, model registries, artifact repositories, and modern MLOps practices.
• Experience deploying LLM or Generative AI applications is a strong advantage, along with excellent problem-solving, communication, and collaboration skills.
• High autonomy & ownership: We give you the freedom to own your work and trust you to make the best decisions for your projects.
• Top-tier talent: Join a team of industry experts and highly skilled professionals who are as passionate as you are about innovation.
• Unlimited growth potential: We support your ambition with plenty of room for personal and professional growth within the company.
• Flexible, remote work: Work from anywhere up to 30 days, in an environment that values flexibility and work-life balance.
• A supportive culture: You’ll be part of a team that encourages, motivates, and celebrates success together.