AI Engineer

Guidehouse Inc.; McLean (VA); On-site; USD 98,000 - 163,000; Full time

Benefits offered by this job

Medical insurance

Dental & Vision Insurance

Parental Leave

401(k) Retirement Plan

Life Insurance

Student Loan PayDown

Tuition Reimbursement

Professional Development

Job summary

Guidehouse Inc. is seeking an AI Engineer in McLean, VA (onsite) to build, test, and deploy AI applications and services.

The work spans data and ML pipelines, retrieval-augmented generation, agentic workflows, and the integration of LLM capabilities into production-ready systems. Responsibilities include developing data/ML pipelines, packaging models for APIs, and standing up cloud resources across environments with CI/CD and IaC tooling.

Qualifications

• US Citizenship is required.
• Bachelor’s degree is required.
• 3 years of experience in software, data, or ML engineering.
• 1 year hands-on experience with Generative AI and/or agentic patterns such as RAG, function/tool calling, and prompt orchestration.
• Proficiency with at least one major cloud (AWS, Azure, or GCP) plus modern DevOps practices including Git, CI/CD, containerization, and infrastructure as code.
• Familiarity with vector databases and embeddings, along with LLM application frameworks.
• Ability to troubleshoot production systems using logs, metrics, and traces; write clear documentation and runbooks; and collaborate effectively across teams.
• Growth mindset with interest in expanding responsibilities into broader architecture over time.

Responsibilities

• Build, test, and deploy AI applications and services by translating solution designs into demo-ready components.
• Develop data and ML pipelines, including ingest, transform, feature stores, and vector indexes, connected to RAG and agentic workflows.
• Package and serve models (LLMs and ML) through APIs and microservices using containers and orchestration tools.
• Stand up and maintain cloud/AI platform resources across environments (AWS/Azure/GCP/Databricks), with CI/CD and IaC.
• Integrate AI capabilities into applications, including prompt orchestration and tool calling.
• Partner with data scientists, platform engineers, and product teams to iterate on use cases and deliver MVPs.
• Support demos, technical documentation, and proposal content.
• Apply responsible AI practices and follow security/compliance requirements.

Education

Bachelor’s degree

Tools

Docker

Kubernetes

Terraform

CI/CD

Git

Vector databases

Embeddings

APIs

Job description

Guidehouse Inc. is seeking an AI Engineer in McLean, VA (onsite) to build, test, and deploy AI applications and services. The work spans data and ML pipelines, retrieval-augmented generation, agentic workflows, and the integration of LLM capabilities into production-ready systems.

Key Responsibilities

• Build, test, and deploy AI applications and services by translating solution designs and reference architectures into demo-ready components.
• Develop data and ML pipelines, including ingest, transform, feature stores, and vector indexes, and connect them to RAG and agentic workflows.
• Package and serve models (LLMs and traditional ML) through APIs and microservices using containers and orchestration tooling such as Docker and Kubernetes.
• Stand up and maintain cloud and AI platform resources across environments including AWS, Azure, GCP, Palantir, and Databricks, with support for CI/CD, infrastructure as code (for example, Terraform), secrets management, and observability.
• Integrate AI capabilities into applications and services, including prompt orchestration, tool/function calling, embeddings, and fine-tuning.
• Partner with data scientists, platform engineers, and product teams to iterate on use cases, deliver POCs/MVPs, and harden solutions for scale.
• Support demos, technical documentation, and solution content for proposals and pitch materials.
• Apply responsible AI practices and follow security and compliance requirements across both commercial and public sector environments.

Required Qualifications

• US Citizenship is required.
• Bachelor’s degree is required.
• Minimum 3 years of experience in software, data, or ML engineering, including building and operating cloud-native services.
• Minimum 1 year of hands‑on experience with Generative AI and/or agentic patterns such as RAG, function/tool calling, and prompt orchestration.
• Proficiency with at least one major cloud (AWS, Azure, or GCP) plus modern DevOps practices including Git, CI/CD, containerization, and infrastructure as code.
• Familiarity with vector databases and embeddings, along with LLM application frameworks.
• Ability to troubleshoot production systems using logs, metrics, and traces; write clear documentation and runbooks; and collaborate effectively across teams.
• Growth mindset with interest in expanding responsibilities into broader architecture over time.

Technologies

• AWS, Azure, GCP, Palantir, Databricks
• Docker, Kubernetes
• Terraform, CI/CD, IaC, Git
• Vector databases, embeddings
• LLM application frameworks
• Retrieval‑augmented generation (RAG), prompt orchestration, tool/function calling, fine‑tuning
• Microservices, APIs

Benefits

• Medical, Rx, Dental & Vision Insurance
• Personal and Family Sick Time & Company Paid Holidays
• Position may be eligible for a discretionary variable incentive bonus
• Parental Leave and Adoption Assistance
• 401(k) Retirement Plan
• Basic Life & Supplemental Life
• Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts
• Short-Term & Long-Term Disability
• Student Loan PayDown
• Tuition Reimbursement, Personal Development & Learning Opportunities
• Skills Development & Certifications
• Employee Referral Program
• Corporate Sponsored Events & Community Outreach
• Emergency Back-Up Childcare Program
• Mobility Stipend

Additional Information

• Salary: USD 98,000 - 163,000 per yearly
• Location: McLean, VA (onsite)
• Travel Required: Up to 10%
• Clearance Required: None
• JOB FAMILY: Engineering Consulting
• 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 (for example AWS/Azure/GCP, Databricks, Kubernetes); experience contributing to client‑facing engineering in consulting or product environments; Master’s degree
• WHAT WE OFFER: A comprehensive, total rewards package including competitive compensation and a flexible benefits package supporting a diverse and supportive workplace