Senior ML Operations (MLOps) Engineer
Build and operate scalable ML infrastructure for deploying models to IoT sleep devices. Own end-to-end pipelines, optimize performance, and collaborate cross-functionally. Requires 5+ years in ML ops, Python, AWS, and production ML deployment.
About the job
Responsibilities
- Pioneer cutting-edge ML technologies, integrating them into products and processes for health monitoring.
- Own design and operation of robust ML infrastructure, building scalable data, model, and deployment pipelines.
- Partner with R&D, firmware, data, and backend teams for reliable ML inference at scale.
- Optimize compute, storage, and deployment resources for training and inference.
- Develop tooling, microservices, and frameworks for data processing, experimentation, and deployment.
Requirements
- 5+ years software engineering experience focused on ML infrastructure, distributed systems, or large-scale data processing in Python (e.g., PyTorch, TensorFlow).
- Hands-on experience with ML workflow orchestration and CI/CD pipelines for model deployment.
- Experience shipping ML models to production at scale, handling telemetry, monitoring, and feedback loops.
- Strong experience with AWS (Lambda, ECS, DynamoDB, CloudWatch) or equivalent for serving and monitoring ML systems.
- Adaptive problem-solving in fast-paced, collaborative environments.
Nice-to-Haves
- Expertise in real-time ML workflows and streaming systems (e.g., Kinesis, Kafka, Flink).
- Optimizing ML infrastructure for efficiency, latency, and cloud cost.
- Understanding of secure ML operations, privacy, and compliance for health/IoT data.
- Familiarity with health, wellness, or IoT domains, especially wearables or medical-grade devices.
Skills
Python, PyTorch, TensorFlow, AWS, AWS Lambda, ECS, DynamoDB, CloudWatch, CI/CD, Ml Workflows, Kubernetes, Kafka, Kinesis, Flink
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