MLOps Engineer

Company Description

VOPAIS architects transformative digital solutions that bridge the gap between advanced technology and human experience. We create intuitive applications that solve complex challenges and bring smiles to users' faces. Our company values integrity, transparency, and transformation, offering premium team deployment solutions to seamlessly integrate into your existing infrastructure. We are committed to crafting digital experiences that empower organizations and their people to thrive in the evolving technological landscape.

Role Description

This is a full-time remote role for an MLOps Engineer. The MLOps Engineer will be responsible for designing, implementing, and managing machine learning infrastructure and pipelines. Day-to-day tasks include optimizing ML models, automating deployments, monitoring and maintaining ML systems, and collaborating with data scientists and software engineers. The role also involves ensuring the scalability, reliability, and security of ML systems.

Key Responsibilities

• ML Pipeline Design & Automation

o Build and maintain CI/CD & CT (Continuous Training) pipelines for ML models using Azure DevOps and Databricks Asset Bundles.

o Automate data preprocessing, training, inference and retraining workflows for large-scale ML deployments.

o Implement incremental backfills and rolling window retraining for time-series forecasting.

• Deployment & Infrastructure

o Design job clusters and compute policies in Databricks for optimal cost-performance trade-offs.

o Implement multi-environment deployment flows (Dev → QA (stage) → Prod) with approvals and rollback strategies.

o Deploy ML models to production with monitoring hooks for performance and drift detection.

• Data & Model Governance

o Integrate with Unity Catalog for secure, compliant data and model storage.

o Set up model versioning, lineage tracking and reproducibility using MLflow.

o Establish dataset and feature versioning using tools like Databricks Feature Store.

• Monitoring & Observability

o Implement structured logging for model metrics, system performance and data quality checks.

o Integrate monitoring tools (e.g., Azure Application Insights) for alerting and dashboards.

o Develop automated retraining triggers based on performance degradation

Required Skills & Experience:

Core MLOps Skills:

• ML pipeline automation (Azure DevOps, GitHub Actions).

• Databricks (Asset Bundles, Unity Catalog, Feature Store).

• Model registry and experiment tracking (MLflow, Weights & Biases or similar).

• Cloud platforms (Azure mandatory).

Programming & Tools:

• Python (pandas, PySpark, scikit-learn, Prophet, ML/DL frameworks).

• Bash/PowerShell scripting.

• Git and branching strategies for ML projects.

Testing & Quality:

• Data validation, schema enforcement and model testing frameworks.

• CI/CD quality gates for model performance and bias/fairness checks.

Soft Skills:

• Strong communication and stakeholder management.

• Experience guiding Data Scientists through productionization.

• Ability to work on multiple concurrent projects in a fast-paced environment.

Good to Have:

• Experience with time-series forecasting at scale (e.g., Prophet, Sarima, XGBoost).

• Experience in retail demand forecasting and/or energy sector analytics.

• Knowledge of feature engineering at scale with distributed systems.

Qualifications

• Experience with MLOps, Machine Learning, and DevOps
• Skills in Python, TensorFlow, PyTorch, or other ML frameworks
• Experience with cloud platforms such as AWS, Azure, or Google Cloud
• Proficiency in CI/CD, containerization, and orchestration tools (e.g., Docker, Kubernetes)
• Strong collaboration and communication skills
• Ability to work independently and remotely
• Experience with monitoring and logging tools (e.g., Grafana, Prometheus)
• Bachelor's degree in Computer Science, Engineering, or a related field