Staff Machine Learning Engineer
Staff Machine Learning Engineer responsible for architecting and operating production-grade ML systems, NLP and Generative AI applications, and end-to-end MLOps on AWS. The role requires 8+ years of experience, strong software engineering fundamentals, and expertise in observability and scalable model deployment.
About the job
Responsibilities
- Architect, develop, and deploy complex, production-grade machine learning systems and data pipelines, particularly for NLP and Generative AI applications.
- Apply AI to business challenges including sentiment analysis, content moderation, product recommendations, and personalized search.
- Identify and address high-impact technical challenges and technical debt in ML and data infrastructure.
- Provide technical mentorship and promote engineering excellence, maintainability, and best practices.
- Collaborate with Data Scientists, Product Managers, and engineering teams to translate business requirements into ML solutions.
- Implement and oversee MLOps practices, including automated CI/CD pipelines, model monitoring, and governance.
- Build reliable, reproducible, and performant systems at scale.
- Implement observability frameworks to detect and diagnose model drift, data-quality anomalies, and production performance degradation.
Requirements
- 8+ years of experience in Machine Learning Engineering, Applied Machine Learning, or a related field, with experience building and maintaining production models.
- Expert proficiency with AWS for MLOps, including Amazon SageMaker, Amazon S3, AWS Step Functions, AWS CloudFormation, Amazon CloudWatch, Amazon MSK, and Amazon Bedrock.
- Experience building and deploying scalable NLP solutions, including handling sarcasm detection, polysemy, and multilingual data.
- Experience with supervised and unsupervised learning, deep learning, and Generative AI techniques including LLMs, RAG, and prompt engineering.
- Proficiency with PyTorch, TensorFlow, and scikit-learn, including adapting and tuning open-source or pretrained models.
- Strong software engineering fundamentals, including design patterns, data structures, testing, security, and version control.
- Experience with CI/CD and regression testing.
- Experience applying model observability practices for issue detection and root-cause analysis.
- Ability to translate complex business problems into viable technical solutions and communicate findings to non-technical stakeholders.
Skills
AWS, Amazon Sagemaker, Amazon S3, Aws Step Functions, Aws Cloudformation, Amazon Cloudwatch, Amazon Msk, Amazon Bedrock, NLP, Generative AI, LLMs, RAG, Prompt Engineering, PyTorch, TensorFlow
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