AI Developer
On-site · Chesapeake, Virginia, United States
Job Summary
Design, build, and deploy production AI applications on Google Cloud Platform; develop agentic AI applications using modern agent development kits and frameworks (e.g., Google Agent Development Kit, Gemini Agent Platform, LangGraph), including multi-agent workflows, tool integration, and human-in-the-loop checkpoints. Build and operate retrieval-augmented generation (RAG) solutions that ground LLMs in enterprise data, including ingestion, embeddings, vector/hybrid search, reranking, and evaluation. Establish and maintain MLOps pipelines for AI workloads (CI/CD, versioning, evaluation harnesses, monitoring, cost controls, rollback). Implement enterprise-grade guardrails for AI deployments (prompt-injection defense, PII handling, content safety, RBAC, audit logging) in collaboration with security and IT teams. Evaluate vendor/partner AI products; perform proofs of concept and bake-offs with clear recommendations. Execute experiments to assess internal product-market fit; deploy production-grade AI agents capable of reasoning and multi-step task execution. Manage code lifecycle from prompt engineering to CI/CD and real-time monitoring; mentor junior engineers; coordinate with IT support for on-call coverage and SOPs. Perform other duties as assigned.
Required Qualifications
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field
- 8+ years of progressive IT experience in large, cross functional teams or projects
- Proficiency in Python; ability to write clean, well-tested, well-documented code
- Experience building and deploying production AI workloads on Google Cloud Platform
- Hands-on experience with generative AI, large language models, and agentic AI frameworks
- Knowledge of MLOps, retrieval-augmented generation (RAG), and applied data science
- Ability to design, implement, and validate AI experiments with clear success metrics and exit criteria
- Experience in retail, supply chain, or large-scale operational environments within retail systems
- Contributions to open-source AI projects or relevant publications
- Experience with vendor/product evaluations, proofs-of-concept, and bake-offs
- Mentoring or leading engineers; collaboration with platform, security, and infrastructure teams
- Understanding of guardrails for AI deployments including prompt injection defense, PII handling, and access controls
- Familiarity with data residency and compliance standards
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