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# AI Engineer

- **Company:** Guidehouse Inc.
- **Location:** McLean, VA, United States
- **Salary:** $98,000.0 - $163,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Frameworks, Microsoft Azure, Continuous Integration, DevOps, Machine Learning, Runbook, Google Cloud, Large Language Models, Generative AI, Git, Containerization, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Terraform, Serverless Computing, Docker, Databricks, Microservices
- **Published:** July 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7b774a3b185cd196

## About the Role

* US Citizenship is required
 * Bachelor's degree is required.
 * Minimum THREE (3) years of experience in software, data, or ML engineering, including building and operating cloud-native services.
 * Minimum ONE (1) year of hands-on experience with Generative AI and/or agentic patterns (e.g., RAG, function/tool calling, prompt orchestration).
 * Proficiency with at least one major cloud (AWS, Azure, or GCP) and modern DevOps practices (Git, CI/CD, containerization, infrastructure as code).
 * Serve as a forward deployed engineer, collaborating with data scientists, platform engineers, and product teams to rapidly iterate on solutions, deliver POCs/MVPs, and harden them for production scale.Familiarity with vector databases and embeddings and LLM application frameworks.
 * Ability to troubleshoot production systems (logs, metrics, traces), write clear documentation/runbooks, and collaborate in cross-functional teams.
 * Growth mindset with interest in expanding into broader architecture responsibilities over time.

What Would Be Nice To Have:

 * Ability to obtain and maintain a Federal or DoD SECRET security clearance; active clearance preferred.
 * Certifications in cloud architecture, DevOps, or AI/ML (e.g., AWS/Azure/GCP, Databricks, Kubernetes).
 * Experience contributing to client-facing engineering in consulting or product environments.
 * Master's degree

## Description

* Implement data and ML pipelines (ingest, transform, feature stores, vector indexes) and wire up retrieval-augmented generation (RAG) and agentic workflows.
 * Package and serve models (LLMs and traditional ML) via APIs and microservices using containers and orchestration (e.g., Docker, Kubernetes).
 * Stand up and maintain cloud resources and AI platforms (AWS, Azure, GCP; Palantir; Databricks), including CI/CD, IaC (e.g., Terraform), secrets, and observability.
 * Integrate AI capabilities (prompt orchestration, tool/function calling, embeddings, fine-tuning) into applications and services.
 * Collaborate with data scientists, platform engineers, and product teams to iterate on use cases, deliver POCs/MVPs, and harden them for scale.
 * Contribute to demos, technical documentation, and solution content for proposals and pitch materials.
 * Follow responsible AI practices and security/compliance requirements across commercial and public sector environments.


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