Responsibilities

The LLM Specialist will drive the design, development, and operationalization of advanced largelanguagemodel capabilities across a cloudbased analytics ecosystem. This role leads innovation efforts around cuttingedge AI, owning the architecture and strategy for finetuning, retrievalaugmented generation (RAG), agentic frameworks, and domainspecific model adaptation. The specialist will guide the development of highimpact prototypes, oversee the evolution of scalable LLM pipelines, and ensure robust governance, security, and performance across all model implementations. Partnering with engineering, product, and data teams, this position provides technical leadership, evaluates emerging LLM technologies, sets best practices, and helps drive transformation through the practical, safe, and effective deployment of generative AI.

Qualifications

5 years with BS/BA; 3 years with MS/MA; 0 years with PhD, 9 years with a HS Diploma

Mandatory Requirements:

Deep expertise in LLM architectures, transformer models, and modern generative AI techniques. Demonstrated experience leading finetuning efforts, parameterefficient training (e.g., LoRA/PEFT), and advanced prompt engineering. Proven ability to design and implement endtoend RAG pipelines, including embedding workflows, retrieval optimization, and vector database integrations. Handson experience with one or more LLM frameworks or orchestration toolchains (such as LangChain, LlamaIndex). Strong Python development skills and experience with distributed compute or GPUaccelerated training environments. Experience architecting and deploying AI/ML or LLM workflows within cloud platforms such as Azure, AWS, or GCP. Solid understanding of MLOps/LLMOps practices, including versioning, CI/CD, automated testing, monitoring, and model governance. Ability to lead technical discussions, mentor team members, and communicate complex AI concepts to diverse audiences. US Citizen with the ability to obtain/maintain a Public Trust clearance Preferred Requirements: Experience implementing multiagent or agentic AI systems for task automation and reasoning. Familiarity with LLM evaluation frameworks, structured benchmarking, or humanintheloop refinement methods (e.g., RLHFstyle workflows). Expertise with advanced retrieval techniques such as hybrid search, graph retrieval, or longcontext optimization. Experience optimizing model inference through quantization, model compression, or model distillation. Background integrating LLM services with largescale analytics environments (e.g., Databricks, Snowflake, Spark). Strong skills in exploratory data analysis, feature engineering, and data modeling to support domainspecific LLM customization. Experience developing innovative prototypes or POCs that leverage stateoftheart generative AI approaches. Exposure to emerging architectures such as mixtureofexperts models, longcontext transformers, or experimental generative frameworks.