ResponsibilitiesThe 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
Minimum of 8 years with BS/BA; Minimum of 6 years with MS/MA; Minimum of 3 years with PhD
Required Skills:
- Deep expertise in LLM architectures, transformer models, and modern generative AI techniques.
- Demonstrated experience leading finetuning efforts, parameterefficient training, 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.
- Ability to obtain/maintain a Public Trust clearance
Preferred Skills:
- 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.
Target Salary Range$104,000 - $166,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individuals experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
