Qualifications
Requirements
- At least 5 years of experience as an ML Engineer, MLOps Engineer, or equivalent.
- Strong proficiency in Python and ML frameworks such as Scikit-learn, XGBoost, PyTorch, or TensorFlow.
- Proven experience with AWS ML services, including SageMaker, Glue, Lambda, Step Functions, S3, and CloudWatch.
- Familiarity with CI/CD and test automation tools (e.g., PyTest, Unittest, GitHub Actions, CodePipeline).
- Experience with infrastructure-as-code, versioning, and model registries.
- Solid understanding of MLOps practices, testing strategies, and production ML requirements.
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or related field.
Preferred Qualifications
- Familiarity with Generative AI, LLM deployment, or RAG pipelines using AWS Bedrock, LangChain, or open-source LLMs.
- Experience with vector databases (e.g., FAISS, OpenSearch), feature stores, and model explainability tools.
- AWS certification (e.g., ML – Specialty, Solutions Architect, or DevOps Engineer) is a plus
Job Types: Full-time, Permanent
Benefits:
- Health insurance
Experience:
- ML/MLOps Engineer: 5 years (Required)
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