Sr Data Scientist - Gen AI ML - Tampa/Irving

• LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.
• Frameworks: LangChain, LlamaIndex, and Hugging Face.
• Orchestration: LangGraph and Multi-Agent Systems (MAS).
• Development: Python, FastAPI, and Asynchronous Programming.
• RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.
• ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.
• Deployment: Docker, Production API management, and LLM monitoring.
• Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.
• Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.
• Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.
• Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.
• Design and deploy high-performance, scalable backend services using FastAPI and Async Python.
• Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.
• Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.
• Containerize and deploy AI services via Docker to production environments.
• 7+ years of experience ; Hands-on experience building and deploying GenAI applications in a production setting.
• Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).
• Experience with vector search, embedding models, and advanced data retrieval patterns.
• Knowledge of model fine-tuning techniques and local LLM quantization/hosting.
• Familiarity with production-grade monitoring, API security, and CI/CD for ML.
• Compensation, Benefits and Duration
• Minimum Compensation: USD 62,000
• Maximum Compensation: USD 217,000
• Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.

None flagged.