Full Stack MLOps Engineer (Databricks / ML Applications)TESTQ Technologies Limited • United States
Full Stack MLOps Engineer (Databricks / ML Applications)
TESTQ Technologies Limited
- United States
- United States
About
Job Type: Contract
Work Mode: Hybrid (2 Days from office)
We are seeking an MLOps Engineer to build and maintain CI/CD pipelines for machine learning models and scripts. This role bridges the gap between data science and production engineering, ensuring ML models are deployed reliably, monitored effectively, and updated seamlessly in production environments.
Key Responsibilities
Build and deploy ML applications on Databricks (end-to-end)
Develop CI/CD pipelines for ML workflows and data pipelines
Work with Databricks (Delta Lake, notebooks, jobs, workflows)
Build APIs (Python/FastAPI) to serve ML models
Containerize and deploy applications using Docker & Kubernetes
Implement monitoring, logging, and model performance tracking
Collaborate with data scientists to productionize models
Required Qualifications
Technical Skills
Programming & Scripting:
Python (advanced) - Primary language for ML and automation
Bash/Shell scripting for automation
YAML for configuration management
Understanding of software engineering best practices
CI/CD Tools:
GitHub Actions, GitLab CI/CD, or Jenkins - Building automated pipelines
Experience with pipeline-as-code concepts
Automated testing frameworks (pytest, unittest)
Containerization & Orchestration:
Docker - Container creation and management (required)
Container registries (Docker Hub, ECR, ACR, GCR)
Experience with AWS, Azure, or GCP (at least one)
Cloud storage (S3, Blob Storage, GCS)
MLOps Tools:
MLflow - Experiment tracking and model registry
DVC (Data Version Control) - Data and model versioning
Weights & Biases, Neptune.ai, or similar (nice to have)
Infrastructure as Code:
Terraform or CloudFormation/ARM templates
Experience managing infrastructure through code
Understanding of state management
Version Control:
Git (advanced) - Branching strategies, merge workflows
GitHub/GitLab/Bitbucket repository management
ML Knowledge
Understanding of ML Workflows:
Familiarity with ML model training and inference
Understanding of model formats (pickle, ONNX, SavedModel, TorchScript)
Knowledge of ML frameworks (scikit-learn, TensorFlow, PyTorch) - not required to build models, but must understand how they work
Awareness of ML lifecycle (training, validation, deployment, monitoring)
Model Serving:
FastAPI or Flask - Building REST APIs for model serving
TensorFlow Serving, TorchServe, or ONNX Runtime (nice to have)
Understanding of model optimization (quantization, pruning)
Monitoring & Observability
Monitoring Tools:
Prometheus & Grafana - Metrics and dashboards
ELK Stack (Elasticsearch, Logstash, Kibana) or similar for logging
ML-Specific Monitoring:
Model drift detection (Evidently AI, Arize, WhyLabs)
Performance metrics tracking
DevOps & Software Engineering
Best Practices:
Documentation standards
Security best practices for ML systems
Testing:
Unit testing, integration testing
Data validation and schema testing
Experience Requirements
3-5+ years in DevOps, MLOps, or software engineering
1-2+ years specifically working with ML model deployment and CI/CD
Proven track record of building and maintaining production ML systems
Experience with cloud platforms and containerization
Hands‑on experience with CI/CD pipeline development
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Languages
- English
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