AI Engineer

Guidehouse•Tysons, VA

1d•$98,000 - $163,000•Onsite

About The Position

This AI Engineer role involves building, testing, and deploying AI applications and services. Key responsibilities include translating solution designs into working components, implementing data and ML pipelines, and packaging/serving models via APIs and microservices using containers and orchestration. The role also requires standing up and maintaining cloud resources and AI platforms, integrating AI capabilities into applications, and collaborating with data scientists, platform engineers, and product teams to iterate on use cases and deliver production-ready solutions. The engineer will contribute to technical documentation and adhere to responsible AI practices and security/compliance requirements. Guidehouse is a global AI-led professional services firm delivering advisory, technology, and managed services to commercial and government sectors, with approximately 18,000 professionals.

Requirements

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).
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.

Nice To Haves

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

Responsibilities

Build, test, and deploy AI applications and services, translating solution designs and reference architectures into working, demo-ready components.
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.
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.

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