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
- 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.