MLOps Engineer (Databricks Specialist)

Job description

This is a remote position.

Position Overview We are seeking a highly skilled MLOps Engineer with deep expertise in the Databricks ecosystem to join our data team for a critical 6-month initiative. In this role, you will bridge the gap between Data Science and Data Engineering, focusing on automating, scaling, and managing the end-to-end lifecycle of our machine learning models. The ideal candidate will have a strong foundation in software engineering and production-grade DevOps practices, specifically optimized for machine learning pipelines (MLOps) within cloud-native Databricks environments. Key Responsibilities

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Pipeline Automation: Design, build, and maintain robust CI/CD and MLOps pipelines for machine learning model training, evaluation, deployment, and batch/real-time scoring using Databricks Jobs and Workflows.

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Model Lifecycle Management: Implement and manage experiment tracking, model registration, versioning, and environment promotion policies using MLflow and Unity Catalog.

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Infrastructure & Optimization: Optimize Databricks clusters and computational workloads for ML training and inference to ensure both cost-efficiency and high performance.

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Data & Feature Engineering: Collaborate with data engineers to build and maintain scalable feature pipelines utilizing Databricks Feature Store / Delta Lake.

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Monitoring & Observability: Establish proactive monitoring frameworks to track model performance, data drift, concept drift, and system health in production environments.

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Collaboration: Partner closely with Data Scientists to transition proof-of-concept (PoC) code into scalable, production-ready ML products.

Requirements

Required Qualifications

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Experience: 6+ years of professional experience in Software Engineering, Data Engineering, or DevOps, with at least 3+ years dedicated to MLOps.

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Databricks Mastery: Hands-on experience architecting ML workflows within Databricks (including MLflow, Unity Catalog, Delta Lake, and Databricks Repos).

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Core Languages: Advanced proficiency in Python and SQL. Strong skills in PySpark are highly desired.

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CI/CD & DevOps: Proven experience building automated deployment pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps.

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Cloud Infrastructure: Familiarity with major cloud environments (AWS, Azure, or GCP) and cloud data infrastructure.

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Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or equivalent practical experience.

Preferred (Nice-to-Have) Skills

- Active Databricks certifications (e.g., Databricks Certified Machine Learning Professional ).

- Experience with Infrastructure as Code (IaC) tools like Terraform.

- Familiarity with containerization (Docker, Kubernetes).

- Exposure to LLMOps or serving GenAI models on Databricks.

Why Work With Us?

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100% Remote: Enjoy the flexibility of a fully remote setup.

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Impactful Work: Own a dedicated stream of work on high-priority ML initiatives over the next 6 months.

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Cutting-Edge Stack: Work on modern, clean Databricks infrastructure.

Originally posted on Himalayas