AI/ML Solutions Engineer
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Job Description
AI / ML Solutions Engineer implementing machine learning workloads with Ray and Anyscale. Collaborating with customers on architecture and operational strategies in a remote setting.
Responsibilities:
- Implement production AI / ML workloads using Ray and Anyscale, such as:
- Distributed model training
- Scalable inference and serving
- Data preprocessing and feature pipelines
- Advise customers on ML system architecture, including:
- Application design for distributed execution
- Resource management and scaling strategies
- Reliability, fault tolerance, and performance tuning
- Guide customers through architectural and operational changes required to adopt Ray and Anyscale effectively
- Partner with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows
- Support CI/CD, monitoring, retraining, and operational best practices
- Help customers transition from experimentation to production-grade ML systems
- Enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance
- Contribute feedback from the field to product, engineering, and education teams
- Help develop reference architectures, examples, and best practices based on real customer use cases
Requirements:
- 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or ML Systems Engineer
- Strong proficiency in Python and experience building production ML systems
- Hands-on experience with distributed systems or scalable ML frameworks (Ray, Spark, Dask, Kubernetes, etc.)
- Experience with one or more of:
- Distributed training (multi-node / multi-GPU)
- Model serving and scalable inference
- Data pipelines and workflow orchestration
- Comfort working directly with customers in a consultative, problem-solving role
- Strong communication skills and ability to explain technical tradeoffs clearly.
Benefits:
- Competitive compensation
- Equity
- Flexible remote work
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