MLOps Engineer

Summary

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.

Minimum Qualifications

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

Preferred Qualifications

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

Description

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.

Responsibilities

Explore, design, and implement advanced ML infrastructure frameworks and tools to accelerate model development and delivery. Champion model observability, incident response, prompt versioning, and feedback loops to ensure continuous model health and performance. Design and maintain automated pipelines for model training, evaluation, versioning, and deployment. Partner closely with ML Engineers and Data Scientists to define metrics, gather requirements, and deliver impactful solutions. Enforce model governance, validation standards, and best practices across teams to ensure reproducibility and compliance. Identify and resolve bottlenecks in ML workflows, improving system reliability, latency, and throughput at scale. Leverage 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 workflows. Use LLM-based tools to assist in drafting technical documentation, runbooks, and incident post-mortems, reducing operational overhead. Apply LLM assistants to support code reviews, test generation, and pipeline debugging to improve overall code quality and team velocity.