AI Engineer

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