MLOps Engineer (Databricks Specialist)

Company: KData AI

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
* 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.
* Model Lifecycle Management: Implement and manage experiment tracking, model registration, versioning, and environment promotion policies using MLflow and Unity Catalog.
* Infrastructure & Optimization: Optimize Databricks clusters and computational workloads for ML training and inference to ensure both cost-efficiency and high performance.
* Data & Feature Engineering: Collaborate with data engineers to build and maintain scalable feature pipelines utilizing Databricks Feature Store / Delta Lake.
* Monitoring & Observability: Establish proactive monitoring frameworks to track model performance, data drift, concept drift, and system health in production environments.
* Collaboration: Partner closely with Data Scientists to transition proof-of-concept (PoC) code into scalable, production-ready ML products.
Requirements
Required Qualifications
* Experience: 6+ years of professional experience in Software Engineering, Data Engineering, or DevOps, with at least 3+ years dedicated to MLOps.
* Databricks Mastery: Hands-on experience architecting ML workflows within Databricks (including MLflow, Unity Catalog, Delta Lake, and Databricks Repos).
* Core Languages: Advanced proficiency in Python and SQL. Strong skills in PySpark are highly desired.
* CI/CD & DevOps: Proven experience building automated deployment pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps.
* Cloud Infrastructure: Familiarity with major cloud environments (AWS, Azure, or GCP) and cloud data infrastructure.
* 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?
* 100% Remote: Enjoy the flexibility of a fully remote setup.
* Impactful Work: Own a dedicated stream of work on high-priority ML initiatives over the next 6 months.
* Cutting-Edge Stack: Work on modern, clean Databricks infrastructure.

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