DescriptionSpecial Requirements- US Citizenship Required to obtain Public Trust
- Active DHS Clearance (preferred)
- Bachelor's degree + 6 years of experience
- 3+ years of experience developing and optimizing solutions using Python or similar, with a strong focus on performance, scalability, and efficiency
- Extensive experience working with vector technology databases, designing and implementing solutions to efficiently store, search, and analyze high-dimensional data for real-time and large-scale applications
- GenAI and Bedrock experience
The Gist...We are seeking a highly skilled Generative AI Engineer to design, develop, and deploy advanced AI-powered solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern cloud-native architectures. This role will focus on integrating LLMs into enterprise systems, building scalable GenAI applications, optimizing data retrieval pipelines, and developing intelligent solutions using vector databases and AWS-native services such as OpenSearch and Bedrock.
The ideal candidate brings strong hands-on engineering expertise in Python, experience architecting and implementing RAG systems, deep understanding of data chunking and embeddings strategies, and practical knowledge deploying production-grade GenAI solutions.
What Your Day Might Include...- Design, build, and deploy LLM-powered applications and intelligent automation solutions for enterprise and mission-focused environments.
- Integrate Large Language Models (LLMs) into existing systems, workflows, products, and enterprise platforms using APIs, orchestration frameworks, and custom pipelines.
- Develop scalable Retrieval-Augmented Generation (RAG) architectures that improve response quality, accuracy, explainability, and contextual relevance.
- Engineer and optimize prompt orchestration, agentic workflows, and inference pipelines for production use.
- Develop prototypes and production-grade solutions leveraging open-source and commercial foundation models.
- Architect and implement robust RAG pipelines, including ingestion, indexing, retrieval, reranking, and response generation.
- Design and optimize data chunking strategies (semantic, recursive, token-based, metadata-aware chunking) to improve retrieval performance and model grounding.
- Create and manage embedding pipelines for structured and unstructured data sources.
- Implement and optimize vector search solutions using vector databases and similarity search technologies.
- Work with vector databases such as OpenSearch, Pinecone, Weaviate, Chroma, FAISS, or similar technologies for scalable retrieval systems.
- Develop data ingestion and knowledge management pipelines to support enterprise search and GenAI applications.
- Build and deploy GenAI solutions in cloud-native environments, with preference for AWS Bedrock, Amazon OpenSearch, and related AWS AI/ML services.
- Integrate LLM applications with enterprise APIs, microservices, databases, and existing application ecosystems.
- Support deployment of scalable and secure AI services using containers, serverless, and modern DevOps/MLOps practices.
- Optimize performance, latency, scalability, and observability of GenAI systems in production.
- Evaluate model performance, retrieval quality, hallucination reduction techniques, and system effectiveness.
- Implement guardrails, grounding strategies, and responsible AI controls for secure and trustworthy solutions.
- Stay current on emerging GenAI technologies, frameworks, and architectures, recommending innovations and improvements.
- Contribute to architecture decisions, technical roadmaps, and GenAI best practices across programs and teams.
Qualifications- Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field
- 5+ years of software engineering or machine learning engineering experience.
- 2+ years of hands-on experience developing Generative AI / LLM-based solutions.
- Strong proficiency in Python and experience building production-grade applications.
- Demonstrated experience integrating LLMs into enterprise systems or applications.
- Hands-on experience designing and implementing RAG architectures.
- Strong experience with data chunking strategies, embeddings, and retrieval optimization.
- Experience with vector databases and semantic search implementations.
- Experience with GenAI frameworks and tooling such as LangChain, LlamaIndex, Haystack, or similar.
- Experience with APIs, microservices, and scalable software architectures.
Preferred Qualifications- Experience with AWS Bedrock, Amazon OpenSearch, and broader AWS AI/ML ecosystem.
- Experience working with foundation models such as Claude, Llama, Mistral, OpenAI, or similar.
- Familiarity with fine-tuning, model evaluation frameworks, and prompt engineering techniques.
- Experience with MLOps/LLMOps, CI/CD pipelines, Docker, Kubernetes, and cloud deployment patterns.
- Knowledge of security, governance, and responsible AI considerations for enterprise GenAI implementations.
- Experience supporting federal, regulated, or enterprise-scale environments is a plus.
