Application Programming Interface (API), Artificial Intelligence (AI), Best Practices, Cloud Computing, Computer Programming, Continuous Deployment/Delivery, Continuous Integration, Data Science, Docker, Engineering, Git, Machine Learning, Management Strategy, Memory Hardware, Memory Management, Modeling Languages, Open Source, Production Systems, Python Programming/Scripting Language, REST (Representational State Transfer), Software Engineering
Job ID: 97874-1
Job Title: Machine Learning Engineer
Location: Sunrise, FL
Duration: 6 months + possible extension
Rate Range: $44 - $46/hour on W2/ C2C (All inclusive)
We are seeking an experienced Machine Learning Engineer to design, build, and deploy production-grade Generative AI solutions powered by Large Language Models (LLMs). The ideal candidate will have hands-on experience with LangChain, LangGraph, Python, ML engineering, and deploying scalable AI applications in production.
Required Skills
- 5+ years of Machine Learning Engineering experience
- Strong experience with Large Language Models (OpenAI, Anthropic, Llama, Mistral, etc.)
- Hands-on experience with LangChain and LangGraph
- Strong Python programming skills
- Experience building and deploying production-ready AI/ML applications
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- LLM orchestration and agentic workflows
- LLM memory architecture and state management
- Vector Databases (Pinecone, ChromaDB, Weaviate, FAISS, Milvus)
- REST APIs (FastAPI/Flask)
- Git and CI/CD
- Docker and Kubernetes
- ML engineering best practices
Nice to Have
- Google Cloud Platform (Vertex AI, BigQuery, GCS)
- MLOps
- Model monitoring
- MLflow
- Airflow
- Data Science background
- Fine-tuning LLMs
- Hugging Face
- Transformers
Responsibilities
- Design, develop, and deploy Generative AI applications using LLMs.
- Build agentic AI workflows using LangChain and LangGraph.
- Develop and productionize ML models and AI applications.
- Implement LLM memory and state management strategies.
- Optimize prompts, retrieval pipelines, and orchestration logic.
- Integrate open-source and commercial LLMs.
- Deploy, monitor, and maintain AI/ML systems in production.
- Collaborate with product, platform, and engineering teams.