AI Engineer (full-stack)

Guidehouse is a consultancy firm specializing in data science. They are seeking an AI Engineer to build, test, and deploy AI applications, implement data and ML pipelines, and collaborate with teams to deliver innovative solutions.

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

Requirements:

• Bachelor's degree from an accredited college/university
• Must be able to OBTAIN and MAINTAIN a Federal or DoD 'PUBLIC TRUST'
• Based on our contractual obligations, candidate must be located within the United States and US Citizen
• Minimum TWO (2) years of experience in software, data, or ML engineering, including building and operating cloud-native services. (1 year of internship/paid work thru college may be utilized as part of the 2 years)
• Minimum SIX (6) months 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)
• Strong programming skills in Python and/or TypeScript/JavaScript; comfort working with APIs, SDKs, and common data formats
• 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
• 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
• Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred
• Master's degree or equivalent experience in a relevant field