LLM Specialist

eSimplicity•Columbia, MD

5d•$104,900 - $145,000•Remote

About The Position

eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow.

Requirements

Must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation.
Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field, with 5+ years of relevant experience in software engineering, systems engineering, AI/ML, or a related discipline.
Experience designing, developing, integrating, and supporting Large Language Model (LLM) and Generative AI solutions, including transformer-based architectures.
Experience with LLM fine-tuning, prompt engineering, and model evaluation, including techniques such as LoRA, PEFT, or similar approaches.
Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions, including embeddings, vector databases, retrieval pipelines, and search optimization.
Strong proficiency in Python and experience with LLM frameworks such as LangChain, LlamaIndex, or comparable technologies.
Experience developing and deploying AI/ML solutions in cloud environments such as AWS, Azure, or GCP, including scalable model training, inference, and performance optimization.
Knowledge of MLOps/LLMOps practices, including source control, CI/CD, automated testing, monitoring, model lifecycle management, security, and responsible AI practices.

Nice To Haves

Experience implementing multi-agent or agentic AI systems for task automation and reasoning.
Familiarity with LLM evaluation frameworks, structured benchmarking, or human-in-the-loop refinement methods (e.g., RLHF-style workflows).
Expertise with advanced retrieval techniques such as hybrid search, graph retrieval, or long-context optimization.
Experience optimizing model inference through quantization, model compression, or model distillation.
Background integrating LLM services with large-scale analytics environments (e.g., Databricks, Snowflake, Spark).
Strong skills in exploratory data analysis, feature engineering, and data modeling to support domain-specific LLM customization.
Experience developing innovative prototypes or POCs that leverage state-of-the-art generative AI approaches.
Exposure to emerging architectures such as mixture-of-experts models, long-context transformers, or experimental generative frameworks.

Responsibilities

Drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem.
Lead innovation efforts around cutting-edge AI, owning the architecture and strategy for fine-tuning, retrieval-augmented generation (RAG), agentic frameworks, and domain-specific model adaptation.
Guide the development of high-impact prototypes, oversee the evolution of scalable LLM pipelines, and ensure robust governance, security, and performance across all model implementations.
Partner with engineering, product, and data teams to provide technical leadership, evaluate emerging LLM technologies, set best practices, and help drive transformation through the practical, safe, and effective deployment of generative AI.