AI/ML Engineer

AI/ML Engineer

Location: Mason, OH / Woodland Hills, CA (Onsite)

Duration: Full-time only

Must Have Technical/Functional Skills:

• Python (Expert level)
• Machine Learning & Model Training
• Training, evaluation, fine tuning
• Tagging and labeling workflows
• Generative AI & LLMs
• Prompt engineering for LLM-based applications
• Document Processing
• Document extraction, parsing, and chunking
• Handling structured & unstructured data
• Embeddings & Vector Search
• Embedding generation
• Vector database integration
• Databases
• Vector Databases
• MongoDB
• Production-grade ML Engineering
• Scalable, production-ready ML/GenAI solutions

Roles & Responsibilities

This role is for a hands-on Data Science Engineer who will design, build, and deploy production-grade Machine Learning and Generative AI solutions. The candidate must have strong Python expertise and practical experience taking ML and GenAI use cases from development to deployment.

The role focuses heavily on LLM-based applications, including prompt engineering, document processing pipelines, and embedding-based search solutions. The engineer will work with both structured and unstructured data, building pipelines for document extraction, parsing, and chunking, and integrating ML models with Vector Databases and MongoDB.

An ideal candidate is someone who understands end-to-end ML workflows—from data preparation, tagging, and labeling, through model training, evaluation, and fine-tuning—while ensuring solutions are scalable, high quality, and production ready.

• Not a pure data analyst → this is an engineering-focused ML/GenAI role

• Not theoretical AI → requires real-world deployment experience

• Strong fit for candidates with backgrounds in:

• ML Engineering
• Applied Data Science
• GenAI / LLM application development

Key Responsibilities

• Design and implement AI/ML solutions using Python and modern ML frameworks
• Develop and optimize Prompt Engineering strategies for LLM-based systems
• Build and deploy Retrieval-Augmented Generation (RAG) pipelines
• Integrate LLMs via APIs (Azure OpenAI preferred) into enterprise applications
• Develop and orchestrate Agentic AI workflows with tool/function calling
• Implement vector search solutions using Vector Databases
• Ensure CI/CD integration and cloud deployment (Azure preferred)
• Establish observability, monitoring, and evaluation frameworks for AI systems
• Collaborate with cross-functional teams to deliver production-ready AI features