AI ML Engineer
Job Location:
Job Location:
Lakeland, FL - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
Yesterday
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
W2 Role only No visa sponsorship at this time
Role: AI ML Engineer
Location : Lakeland FL 33811
Remote Role
Job Description:
* Local-First AI Expertise: Proven track record deploying and optimizing open-source LLMs (e.g. LLaMA Mistral) in non-cloud restricted or air-gapped private infrastructures
* Deep Framework Proficiency: Heavy hands-on experience with PyTorch Hugging Face and orchestration layers like LangChain LlamaIndex or equivalent frameworks
* Vector and Retrieval Mastery: Direct experience engineering production-grade RAG architectures embeddings semantic search and local vector databases (e.g. FAISS Qdrant Milvus Chroma)
* Containerization and Compute Infrastructure: Strong experience containerizing AI workloads via Docker/Kubernetes and managing dedicated GPU-based compute environments
* Advanced ML Concepts: Solid understanding of fine-tuning techniques (LoRA/QLoRA) versus prompt engineering and model quantization formats (GGUF AWQ EXL2)
* Autonomy: Ability to build test and iterate rapidly in an isolated development sandbox with zero dependency on third-party cloud APIs
* Experience operating within heavily regulated or compliance-driven industries (e.g. high-governance data environments fintech or legal-tech)
* Familiarity with local-first agentic workflows Model Context Protocol (MCP) or building fully internal developer copilots and autonomous knowledge systems
Location : Lakeland FL 33811
Remote Role
Job Description:
* Local-First AI Expertise: Proven track record deploying and optimizing open-source LLMs (e.g. LLaMA Mistral) in non-cloud restricted or air-gapped private infrastructures
* Deep Framework Proficiency: Heavy hands-on experience with PyTorch Hugging Face and orchestration layers like LangChain LlamaIndex or equivalent frameworks
* Vector and Retrieval Mastery: Direct experience engineering production-grade RAG architectures embeddings semantic search and local vector databases (e.g. FAISS Qdrant Milvus Chroma)
* Containerization and Compute Infrastructure: Strong experience containerizing AI workloads via Docker/Kubernetes and managing dedicated GPU-based compute environments
* Advanced ML Concepts: Solid understanding of fine-tuning techniques (LoRA/QLoRA) versus prompt engineering and model quantization formats (GGUF AWQ EXL2)
* Autonomy: Ability to build test and iterate rapidly in an isolated development sandbox with zero dependency on third-party cloud APIs
* Experience operating within heavily regulated or compliance-driven industries (e.g. high-governance data environments fintech or legal-tech)
* Familiarity with local-first agentic workflows Model Context Protocol (MCP) or building fully internal developer copilots and autonomous knowledge systems
