Join kdata ai as a highly skilled MLOps Engineer to bridge the gap between Data Science and Data Engineering. Automate, scale, and manage machine learning model lifecycles using Databricks. Collaborate with Data Scientists to transition proof-of-concept code into production-ready ML products.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Position Type: 6-Month Contract (Performance-based multi-term renewals)
Experience Level: Intermediate to Senior (6+ years)
Location: Remote
Hourly Rate: $65.00 – $75.00 / hour (Dependent on experience)
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.
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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.
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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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