AI Engineer

Amivero•Reston, VA

89d

About The Position

Amivero’s team of IT professionals delivers digital services that elevate the federal government, whether national security or improved government services. Our human-centered, data-driven approach is focused on truly understanding the environment and the challenge and reimagining with our customer how outcomes can be achieved. Our team of technologists leverage modern, agile methods to design and develop equitable, accessible, and innovative data and software services that impact hundreds of millions of people. As a member of the Amivero team you will use your empathy for a customer’s situation, your passion for service, your energy for solutioning, and your bias towards action to bring modernization to very important, mission-critical, and public service government IT systems. 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.

Requirements

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.
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

Nice To Haves

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.

Responsibilities

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.