AI/ML Ops - Senior Engineer

Key Responsibilities:

• Design and implement ML pipelines for model training, validation, testing, and deployment.
• Automate ML workflows using tools such as MLflow, Kubeflow, Airflow, or similar.
• Deploy machine learning models to production environments (cloud).
• Monitor model performance, drift, and data quality in production.
• Collaborate with data scientists to improve model robustness and deployment readiness.
• Ensure CI/CD practices for ML models using tools like Jenkins, GitHub Actions, or GitLab CI.
• Optimize compute resources and manage model versioning, reproducibility, and rollback strategies.
• Work with cloud platforms AWS and containerization tools like Kubernetes (AKS).
• Ensure compliance with data privacy and security standards (e.g., GDPR, HIPAA).

Required Qualifications:

• Bachelors or Masters degree in Computer Science, Engineering, or related field.
• 5+ years of experience in DevOps, Data Engineering, or ML Engineering roles.
• Strong programming skills in Python; familiarity with R, Scala, or Java is a plus.
• Experience with automating ML workflows using tools such as MLflow, Kubeflow, Airflow, or similar
• Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn, or XGBoost.
• Experience with ML model monitoring and alerting frameworks (e.g., Evidently, Prometheus, Grafana).
• Familiarity with data orchestration and ETL/ELT tools (Airflow, dbt, Prefect).

Preferred Qualifications:

• Experience with large-scale data systems (Spark, Hadoop).
• Knowledge of feature stores (Feast, Tecton).
• Experience with streaming data (Kafka, Flink).
• Experience working in regulated environments (finance, healthcare, etc.).
• Certifications in cloud platforms or ML tools.

Soft Skills:

• Strong problem-solving and debugging skills.
• Excellent communication and collaboration with cross-functional teams.
• Adaptable and eager to learn new technologies.