ML Engineer

Cynet Systems • Woodland Hills, CA

1d

About The Position

We are seeking an experienced ML Engineer with a strong background in Python development, Machine Learning model development and training, Generative AI/LLM solutions, and Vector Databases. The ideal candidate will have expert-level proficiency in Python and extensive experience in designing and developing scalable ML and Generative AI solutions. This role involves building robust pipelines for data extraction, parsing, and chunking, as well as training, evaluating, and fine-tuning ML models. You will be responsible for implementing embedding generation, vector search solutions, and integrating ML models with Vector DBs and MongoDB, ensuring code quality, scalability, and production readiness.

Requirements

• 8+ years of Python development experience.
• 5+ years of Machine Learning model development and training.
• 3+ years of Generative AI / LLM solution development.
• 3+ years of Vector Database, Embeddings, and RAG implementation.
• 2+ years of A2A (Agent-to-Agent) and MCP (Model Context Protocol) implementation experience.
• 2+ years of AI Agent and Multi-Agent System development experience.
• Expert-level proficiency in Python.
• Strong experience in model training, evaluation, and tagging workflows.
• Hands-on experience with document extraction and chunking techniques.
• Solid understanding of ML algorithms and Generative AI concepts.
• Experience working with Vector Databases and/or MongoDB.

Nice To Haves

• LangChain / LangGraph
• LlamaIndex
• OpenAI / Azure OpenAI
• A2A Agent Frameworks
• Vector DBs (Pinecone, Chroma, Weaviate, Milvus, FAISS)
• Hugging Face
• MLflow
• FastAPI
• Docker & Kubernetes

Responsibilities

• Design and develop scalable ML and Generative AI solutions.
• Develop and deploy machine learning and GenAI solutions using Python.
• Design and optimize prompt engineering strategies for LLM-based applications.
• Build document extraction, parsing, and chunking pipelines for structured and unstructured data.
• Train, evaluate, and fine-tune ML models; manage tagging and labeling workflows.
• Implement embedding generation and vector search solutions.
• Integrate ML models with Vector DBs and MongoDB.
• Ensure code quality, scalability, and production readiness.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed