Machine Learning Engineer / Data Scientist
New
C
Clinician NexusHealth Care
This is a remote role; however, we only operate in the following states: AZ, CA, CO, FL, GA, IL, IN, MA, MI, MN, MO, NJ, NY, NC, OH, PA, TX and WI.Full-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
- DockerPythonGitMLFlowPyTorchscikit-learnLLMGenerative AILangChain
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field.
- 5+ years of relevant experience for Bachelors degree holders, or 3+ years for Masters degree holders.
- Fluent in Python with 3+ years of coding experience.
- Strong software development practices for writing maintainable, production-ready code.
- Solid understanding of LLM architectures and Generative AI.
- Hands-on experience building and evaluating RAG pipelines.
- Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex).
- Proficiency in Scikit-learn, PyTorch, NumPy, and Pandas.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker.
- Strong understanding of model evaluation metrics for traditional ML and LLM-based systems.
- Experience with MLFlow and the model development lifecycle.
- Proficiency in Git and SDLC best practices.
Responsibilities
- Design, develop, and deploy ML solutions ranging from traditional ML applications to LLM-based systems.
- Write clean, maintainable, production-quality Python code that integrates with existing infrastructure.
- Work with large datasets to clean, preprocess, and analyze data for quality and integrity.
- Implement and optimize algorithms using best practices in deep learning and statistical analysis.
- Collaborate with stakeholders to understand requirements and deliver actionable, data-driven solutions.
- Develop and maintain scalable pipelines for data processing, model training, versioning, and monitoring.
- Evaluate and tune ML and LLM-specific model performance.
- Communicate insights and model performance to technical and non-technical audiences.
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