Description

You will build, deploy, and operate data and ML systems in production, with a focus on the MLflow ecosystem and MLOps tooling.

This role is remote.

Responsibilities

• Build and maintain data pipelines and ML lifecycle infrastructure using workflow orchestration tools like Airflow and Kubeflow.
• Design and implement scalable systems for model tracking, experiment management, and deployment using MLflow and SageMaker.
• Develop and optimize data storage and retrieval solutions using cloud platforms such as Snowflake, DynamoDB, and OpenSearch.
• Containerize applications and manage cloud infrastructure using Docker, Kubernetes, and GitOps tools like Argo CD.
• Collaborate with engineering teams to integrate LLM/agent frameworks and model evaluation tools into production workflows.

Required Skills

• 5+ years of experience building, deploying, and operating data and ML systems in production.
• Strong proficiency in Python, Java, and SQL with solid software engineering fundamentals.
• Hands-on experience with MLflow and MLOps tooling (Metaflow, SageMaker, Galileo).
• Proficiency in workflow orchestration (Airflow, Kubeflow) and cloud data platforms.
• Working knowledge of Docker, Kubernetes, and CI/CD pipelines (Argo CD).
• Experience with at least one major cloud platform (AWS, GCP, or Azure).
• Understanding of distributed computing and streaming frameworks (Spark, Flink, Kafka).
• Strong foundation in ML/AI concepts including statistics, probability, and optimization.

Preferred Skills

• Experience with LLMs, generative AI workflows, RAG, and vector databases.
• Familiarity with GPU-based implementation and AI-assisted development tools.

Key Skills

Education

Any Graduate