Machine Learning Engineer

Role: Machine Learning Engineers Location: St. Louis MO Type: Contract This position is ideal for someone eager to apply practical ML and LLM techniques in production, leveraging Databricks, Python, and modern vector database frameworks. WhatYou’ll Do • Develop Retrieval & Embedding Pipelines: • Build and deploy pipelines that transform enterprise documents (Confluence pages, OneDrive files, internal reports) into structured and vectorized data for semantic retrieval. • Use tools like Databricks MLflow, MosaicML, and LangChain to orchestrate workflows. • Integrate LLMs with Knowledge Bases: • Design and implement Retrieval-Augmented Generation (RAG) systems to ground LLM outputs in enterprise data. • Collaborate with AI agents on Databricks to provide contextualized responses from internal knowledge stores. Experiment & Optimize Models: • Evaluate different embedding models, fine-tuning strategies, and retrieval mechanisms for efficiency, scalability, and accuracy. • Contribute to prompt engineering, model benchmarking, and performance tracking. • Collaborate Across Disciplines: • Work closely with Data Engineers on ingestion and cleaning pipelines, and with Software Engineers on API integration and front-end consumption of ML services. • Operationalize ML Solutions: • Use MLflow to track experiments, automate deployment pipelines, and ensure reproducibility across environments. • Contribute to testing, documentation, and continuous improvement of ML infrastructure. What YouBring • Solid foundation in machine learning, natural language processing, or applied AI. • Proficiency in Python and familiarity with frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers. • Experience with Databricks, MLflow, or MosaicML. • Familiarity with LangChain, LlamaIndex, or similar RAG frameworks. • Understanding of vector databases (e.g., Chroma, Milvus, Pinecone, FAISS). • Experience with API integration and data retrieval from enterprise systems (e.g., Confluence, SharePoint, OneDrive). • Ability to collaborate in a cross-functional engineering team and communicate complex technical concepts clearly. Bonus Skills • Experience fine-tuning or evaluating LLMs (e.g., Llama, MPT, Falcon, or Databricks-hosted models). • Knowledge of OCR pipelines for document ingestion and Databricks Unity Catalog for managing structured data. • Background in cloud infrastructure, containerization (Docker), or CI/CD for ML systems. • Prior work with embedding search optimization, semantic caching, or enterprise AI governance.