onsite
Machine Learning Engineer - II - NAVI
ML Engineer
Machine Learning Engineer II focused on building scalable MLOps pipelines, feature‑store services, and real‑time model serving using Python, deep‑learning frameworks, and cloud infrastructure.
About the role
Key Responsibilities
- Design, develop, and maintain end‑to‑end MLOps pipelines for model training, validation, and deployment.
- Implement and operate a centralized feature‑store, ensuring data quality, versioning, and low‑latency access for online and offline use.
- Build and scale in‑house model serving infrastructure, including real‑time inference APIs and monitoring dashboards.
- Collaborate with data scientists to translate research prototypes into production‑ready services.
- Optimize cloud resources (AWS, Kubernetes) for cost‑effective, high‑throughput model training and serving.
Requirements
- 5+ years of professional experience in machine learning engineering or related fields.
- Strong proficiency in Python and deep‑learning libraries such as TensorFlow or PyTorch.
- Hands‑on experience with MLOps tools and practices, including CI/CD, containerization (Docker), and orchestration (Kubernetes).
- Proven track record deploying models on AWS services (SageMaker, EKS, Lambda, etc.) and implementing monitoring/alerting for production models.
- Solid understanding of feature engineering, data pipelines, and version control for datasets and models.
Skills
pythontensorflowpytorchmlopskubernetesaws