Machine Learning Engineer

Job Description

You will work on mission-critical systems where robustness, monitoring, and maintainability are paramount, leveraging modern MLOps tools, cloud platforms, containerization, and model serving at scale.

• Design and build robust ML pipelines and services for efficient model training, validation, and seamless deployment into production.
• Collaborate closely with data scientists, solution architects, DevOps engineers, and other stakeholders to align components and pipelines with overarching project goals and requirements. Communicate any deviations from target architecture effectively.
• Ensure seamless cloud integration with AWS and Azure cloud services for enhanced performance, scalability, and resource utilization.
• Build reusable infrastructure components applying best practices from both DevOps and MLOps.
• Adhere to stringent security standards and regulatory compliance, particularly when handling confidential and sensitive data within ML systems.
• Design optimal network plans for given Cloud Infrastructure, ensuring compliance with established network security guidelines.
• Monitor model performance in production and implement automated drift detection and retraining pipelines to maintain model accuracy and relevance.
• Optimize models for performance, scalability, and cost efficiency through techniques such as batching, quantization, and hardware acceleration.
• Create detailed documentation and guidelines for the effective use and modification of developed components, fostering knowledge sharing within the team.

• Strong programming skills in Python.
• Deep experience with major ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Hands-on experience with MLOps tools like MLflow, Airflow, TFX, Kubeflow, or BentoML.
• Proven experience deploying models using Docker and Kubernetes.
• Strong knowledge of cloud platforms (AWS/GCP/Azure) and their respective ML services (e.g., SageMaker, Vertex AI).
• Proficiency with data engineering tools including Spark, Kafka, and both SQL/NoSQL databases.
• Solid understanding of CI/CD (Continuous Integration/Continuous Delivery) principles, version control (Git), and infrastructure as code (Terraform, Helm).
• Experience with monitoring and logging tools such as Prometheus, Grafana, and ELK stack.

• Experience with feature stores (e.g., Feast, Tecton) and experiment tracking platforms.
• Knowledge of edge/embedded ML, model quantization, and optimization techniques.
• Familiarity with model governance, security, and compliance considerations in ML systems.
• Exposure to on-device ML or streaming ML use cases.
• Experience leading cross-functional initiatives or mentoring junior engineers.