DATA & AI - Ops - Engineer

Job Description:

Job Description – Data & AI Ops Engineer (MLOps Engineer)
Role Overview
We are looking for an experienced Data & AI Ops Engineer with expertise in MLOps, Data Engineering, and ML deployment pipelines. The role involves designing, automating, and optimizing end-to-end ML workflows — from data preparation to model training, deployment, monitoring, and lifecycle management — across Azure ML, AWS SageMaker, and Google Vertex AI. The ideal candidate will bring strong skills in Python, PySpark, SQL, CI/CD, and containerization, along with hands-on experience in model serving, monitoring, and optimization.
Key Responsibilities
ML Development & Deployment
Implement and manage end-to-end ML pipelines using Azure ML Pipelines, Kubeflow Pipelines, and MLflow.
Support model development with scikit-learn, TensorFlow, and PyTorch, including training, tuning, and serialization (pickle, ONNX, TorchScript).
Deploy models into production using Docker, Azure ML, AWS SageMaker, and Vertex AI with scalable serving frameworks.
MLOps & Automation
Develop CI/CD pipelines for ML workflows using GitHub Actions, MLflow CI/CD integrations, and container registries.
Implement continuous training (CT), continuous integration (CI), and continuous delivery (CD) practices for ML systems.
Automate data ingestion, preprocessing, and feature pipelines with PySpark and SQL.
Model Monitoring & Optimization
Monitor model performance, drift, and data quality in production environments.
Implement logging, alerting, and observability for ML models and pipelines.
Optimize inference performance with ONNX, TorchScript, and TensorRT (optional).
Collaboration & Governance
Partner with Data Scientists, Data Engineers, and DevOps teams to integrate ML models into business workflows.
Ensure compliance with data governance, security, and regulatory policies.
Contribute to the standardization of MLOps frameworks, best practices, and reusable components.
Required Skills & Qualifications
7+ years of experience in Data/AI Engineering, with 3–5 years in MLOps.
Strong programming skills in Python, PySpark, SQL.
Expertise in ML frameworks: scikit-learn, TensorFlow, PyTorch.
Experience with model serialization formats (pickle, ONNX, TorchScript).
Hands-on with CI/CD tools: GitHub Actions, MLflow CI/CD, Kubeflow Pipelines, Azure ML Pipelines.
Experience deploying ML models on Azure ML, AWS SageMaker, and Vertex AI.
Proficiency in Docker and containerized deployments.
Preferred Skills
Familiarity with Kubernetes for scaling ML workloads.
Experience with feature stores and monitoring tools (Feast, WhyLabs, Evidently AI, Prometheus, Grafana).
Knowledge of data governance and compliance (GDPR, HIPAA, etc.).
Exposure to large-scale distributed systems and real-time inference.

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