Machine Learning (MLOps) Engineer

As an MLOps Engineer you will be the backbone of our machine learning infrastructure ensuring that AI/ML systems are reliable scalable and continuously improving in production. You will bridge the gap between data science and engineering driving operational excellence across the full ML lifecycle.

The MLOps Engineer will drive end-to-end quality initiatives across data ingestion model training deployment pipelines and MLOps tooling. This hire will build deploy and optimize AI/ML based applications with a strong emphasis on scalable and production-ready systems. You will establish standard methodologies for model integration deployment and monitoring using CI/CD principles.

Explore design and implement advanced ML infrastructure frameworks and tools to accelerate model development and model observability incident response prompt versioning and feedback loops to ensure continuous model health and and maintain automated pipelines for model training evaluation versioning and closely with ML Engineers and Data Scientists to define metrics gather requirements and deliver impactful model governance validation standards and best practices across teams to ensure reproducibility and and resolve bottlenecks in ML workflows improving system reliability latency and throughput at AI coding assistants and LLM-based tools (e.g. Claude Gemini GitHub Copilot) to accelerate development automate repetitive tasks and improve engineering productivity across ML LLM-based tools to assist in drafting technical documentation runbooks and incident post-mortems reducing operational LLM assistants to support code reviews test generation and pipeline debugging to improve overall code quality and team velocity.

8 years in software engineering with demonstrated experience in large-scale software system design and Degree in Software Engineering Computer Science Statistics Data Mining Machine Learning Operations Research or related track record of shipping and maintaining production-grade ML systems experience with distributed systems databases (SQL/NoSQL) cloud platforms (AWS Azure or GCP) and container orchestration tools such as -on experience with MLOps tooling and platforms such as Ray MLflow Kubeflow SageMaker Vertex AI or in Python and familiarity with ML frameworks such as TensorFlow PyTorch or building and managing CI/CD pipelines for ML workflows using tools such as Jenkins GitHub Actions or understanding of data pipeline orchestration tools such as Airflow Prefect or similar.n

10 years of related experience building high-throughput scalable applications or machine learning models in a production with model monitoring drift detection and observability practices in production cross-functional communication skills with the ability to collaborate effectively across engineering and data science using LLM-based tools such as Claude Gemini or ChatGPT to assist with code generation documentation debugging and workflow ability to critically evaluate and validate LLM-generated outputs ensuring accuracy and reliability before applying them in production incorporating AI-assisted tools into day-to-day engineering workflows with an understanding of their limitations and appropriate use cases.