Full Stack MLOps Engineer (Databricks / ML Applications)

TQUSI0682_5573 - Full Stack MLOps Engineer (Databricks / ML Applications) 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 J-18808-Ljbffr