Job Role: AI/ML Engineer
Job Location: Mason, OH /Woodland Hills, CA
Job Type: Full Time (Day 1 Onsite)
Salary Range: $95000 to $135000/Annum + Full Time Benefits
Must Have Technical/Functional Skills
Job Location: Mason, OH /Woodland Hills, CA
Job Type: Full Time (Day 1 Onsite)
Salary Range: $95000 to $135000/Annum + Full Time Benefits
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
- 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
- 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 (Az ure 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
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