Machine Learning Engineer
New
C
Clinician NexusHealth Care Technology
United StatesFull-TimeMiddle
Salary100,700 - 167,800 USD per year
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Job Details
- Experience
- Bachelor with 5+ years of relevant experience; Master or higher with 3+ years of relevant experience
- Required Skills
- DockerPythonGitMachine LearningMLFlowPyTorchscikit-learnGenerative AI
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
- Bachelor with 5+ years of relevant experience, or Master with 3+ years of relevant experience.
- Fluent in Python with 3+ years of coding experience.
- Strong software development practices in Python, including writing maintainable, testable, production-ready code.
- Solid understanding of LLM architectures and Generative AI.
- Hands-on experience building and evaluating RAG pipelines.
- Experience with LLM orchestration frameworks such as LangChain or LlamaIndex.
- Proficiency in machine learning libraries such as Scikit-learn and PyTorch.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker.
- Strong understanding of model evaluation metrics across traditional ML and LLM-based systems.
- Experience with model management tools such as MLFlow and version control tools such as Git.
Responsibilities
- Design, develop, and deploy ML solutions ranging from traditional ML applications to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents.
- Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure.
- Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity.
- Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis.
- Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights.
- Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring.
- Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance.
- Communicate findings, insights, and model performance to both technical and non-technical audiences.
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