At XiFin, a culture of inclusivity is in our very fabric. We believe that this culture not only creates a more equitable and functional workplace, but also enhances our team members’ work experiences by promoting creativity, innovation, and collaboration.

WHO ARE WE LOOKING FOR?

We are seeking a highly motivated Associate Machine Learning Engineer to join our innovative Advance Data Analytics team. This is an excellent opportunity for someone early in their ML journey who is eager to apply their knowledge to real-world problems in healthcare, life sciences, and revenue cycle optimization.
The ideal candidate is passionate about machine learning, large language models (LLMs), Generative AI (GenAI), and Agentic AI; curious about data; and eager to contribute to the design, deployment, and optimization of production-grade ML solutions that deliver measurable business impact.

WHAT EDUCATION AND EXPERIENCE DO YOU NEED?

A combination of the following education and experience factors will be considered:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field; or equivalent practical experience
• Proficiency in Python and familiarity with ML libraries such as scikit-learn, TensorFlow, or PyTorch
• Solid understanding of ML fundamentals (e.g., supervised/unsupervised learning, overfitting, cross-validation)
• Foundational knowledge of Generative AI, Agentic AI workflows, and LLM architecture (e.g., prompt chaining, RAG patterns, few-shot learning)
• Exposure to technologies such as Neo4j, Vector Databases (e.g., FAISS, Pinecone, Weaviate), LLM fine-tuning, and LangChain or LLM orchestration frameworks
• Experience working with structured data (SQL) and unstructured data (text, embeddings, graphs)
• Familiarity with Git, containerization tools (e.g., Docker), and cloud environments (AWS, Azure, or GCP)
• Excellent communication skills, curiosity, attention to detail, and a growth-oriented mindset

Nice to Haves:

• Internship or project experience involving production ML and LLM systems
• Exposure to MLOps tools (e.g., MLflow, Airflow, SageMaker, or Azure ML)
• Experience in healthcare, genomics, or billing analytics is a plus

• Design, develop, and evaluate machine learning models across healthcare and operational domains, with a growing focus on LLMs, Generative AI (GenAI), and Agentic AI workflows
• Contribute to GenAI and traditional model deployment pipelines, supporting MLOps practices including automation, monitoring, and continuous integration
• Assist in data preparation, prompt engineering, feature engineering, and exploratory data analysis for both structured and unstructured data
• Collaborate cross-functionally with data scientists, software engineers, and domain experts to translate business needs into scalable, AI-powered solutions
• Participate in model validation, safety checks, bias detection, documentation, and performance reviews for traditional and GenAI models
• Stay up-to-date with advancements in ML, LLM fine-tuning, open-source GenAI frameworks, data engineering, and cloud-native AI services
  This is an onsite position at our office in San Diego, CA.