AI Engineer MidSenior AI Agents US Citizens only Federal

United Global Technologies; Menlo Park (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

United Global Technologies is seeking a Mid-to-Senior AI Engineer to design, build, and support production-grade AI Agents and Generative AI solutions for a leading U.S. Department of Energy laboratory.

This hands-on role focuses on building AI agents, RAG pipelines, and data integrations across AWS Bedrock, Google Gemini for Government, and Microsoft Copilot/Copilot. You will mentor a junior engineer while collaborating with security, cloud, and data teams.

Qualifications

• Bachelor's or equivalent in CS or related field.
• 4+ years software engineering or data engineering.
• 2+ years Python development.
• Experience with Generative AI/LLMs.
• Hands-on with AI Agents or RAG.
• Cloud experience (AWS/GCP/Azure).

Responsibilities

• Design and build AI Agents and agentic workflows using LLMs.
• Develop AI agents interacting with data, APIs, and tools.
• Build RAG applications with enterprise data.
• Develop Python services, APIs, and back-end components.
• Create data pipelines for ingestion, embeddings, retrieval.
• Deploy AI apps with DevOps and CI/CD.

Skills

Python development

AI agents

RAG pipelines

LLM apps

API integration

Cloud platforms

Git & CI/CD

Containers

Embeddings

Problem solving

Education

Bachelor's degree in CS or related

Equivalent experience

Tools

LangChain

LangGraph

Semantic Kernel

Docker

Kubernetes

Terraform

OpenSearch

Pinecone

Weaviate

Job description

Federal US Citizens ONLY - NO SUBS, NO Subcontracting, NO outside firms.

AI Engineer – AI Agents & Generative AI

Mid-to-Senior Level-

Hands-on experience with designing and building AI Agents

UGT is hiring a Mid-to-Senior AI Engineer to design, build, and support production-grade AI Agents and Generative AI solutions for a leading U.S. Department of Energy laboratory.

This is a hands-on engineering role focused on building AI agents, Retrieval-Augmented Generation (RAG) solutions, and the data pipelines and integrations that support them. The environment spans AWS Bedrock, Google Gemini for Government, and Microsoft Copilot, providing the opportunity to work across multiple enterprise AI platforms.

The ideal candidate combines strong Python/software engineering skills with practical experience building LLM-powered applications. You do not need to be an expert in every cloud or AI technology listed, but you should have hands-on experience taking Generative AI solutions beyond experimentation and into usable, reliable applications.

The engineer will work closely with cloud, security, data, and business teams and will help mentor a junior AI engineer as the laboratory expands its AI capabilities.

What You'll Do

• Design and build AI Agents and agentic workflows using Large Language Models (LLMs).
• Develop AI agents capable of interacting with enterprise data, APIs, applications, and approved tools.
• Build and enhance RAG applications using enterprise documents and structured/unstructured data.
• Develop Python services, APIs, and backend components supporting AI applications.
• Build data pipelines for document ingestion, parsing, chunking, metadata enrichment, embeddings, indexing, and retrieval.
• Implement vector search and retrieval capabilities to provide reliable grounding for LLM applications.
• Develop solutions using AWS Bedrock, Google Gemini/Vertex AI, Microsoft Copilot/Azure AI, or comparable cloud AI platforms.
• Support the laboratory's centralized AI control plane for model access, monitoring, governance, usage tracking, and cost management.
• Integrate AI solutions with enterprise applications, databases, document repositories, APIs, and other data sources.
• Implement appropriate authentication, authorization, logging, auditing, and data-access controls.
• Develop testing and evaluation processes for AI agents and RAG solutions, including accuracy, retrieval quality, reliability, latency, and cost.
• Monitor and troubleshoot production AI applications and supporting services.
• Containerize and deploy AI applications using modern DevOps and CI/CD practices.
• Collaborate with cybersecurity, cloud engineering, data teams, subject-matter experts, and business stakeholders.
• Document architectures, integrations, workflows, and operational procedures.
• Provide technical guidance and mentorship to a junior AI engineer.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, Artificial Intelligence, or related field, or equivalent professional experience.
• 4+ years of professional software engineering, application development, data engineering, or related technical experience.
• 2+ years of hands-on Python development experience.
• Practical experience developing applications using Generative AI/LLMs.
• Hands-on experience with AI Agents, RAG, or LLM-based application development.
• Experience integrating applications with APIs and enterprise data sources.
• Understanding of embeddings, vector search, prompt engineering, context management, and LLM application patterns.
• Experience with at least one major cloud environment such as AWS, Google Cloud, or Microsoft Azure.
• Familiarity with Git, CI/CD, containers, and modern software development practices.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work independently while collaborating effectively with technical and non-technical stakeholders.

Preferred Qualifications

• Experience with AWS Bedrock, including Bedrock models, Knowledge Bases, Agents, or related services.
• Experience with Google Gemini / Vertex AI.
• Experience with Microsoft Copilot or Azure AI.
• Experience with agent frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or similar.
• Experience with vector databases or search technologies such as OpenSearch, pgvector, Pinecone, Weaviate, Milvus, FAISS, or similar.
• Experience building production RAG pipelines involving document ingestion, chunking, embeddings, retrieval, and evaluation.
• Experience with Docker and/or Kubernetes.
• Familiarity with Terraform or Infrastructure as Code.

• Understanding LLM security, governance, observability, evaluation, and cost management.
• Experience working in DOE, federal government, national laboratory, or another regulated/security-conscious environment.
• Previous experience mentoring junior engineers.