- Location
- McLean, Virginia, United States · published
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Source
- Workable
Description
Overview
Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With 1,700+ team members, 1,500+ AI/data experts, and 100+ prime contracts, we deliver at scale and with purpose.
We’ve been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges.
Position Summary
We have an immediate need for a highly skilled Senior Google AI Engineer. We are growing our Google Cloud AI engineering capability to support our Department of War (DoW) programs. You will design, build, and operationalize production grade AI systems on Google Cloud—accelerating mission outcomes for a high visibility program.
As a Senior Google AI Engineer, you will serve as a hands‑on technical leader for AI solution delivery. You’ll translate mission needs into secure, scalable AI/ML systems; guide data, platform, and application engineers; and ensure solutions meet DoW security and compliance requirements in production. The ideal candidate combines deep GCP/Looker/BigQuery/Vertex AI expertise with strong MLOps, data engineering fluency, and experience delivering in regulated environments.
Responsibilities include, but are not limited to the duties listed below:
- Architect and deliver end‑to‑end AI/ML solutions on Google Cloud using Vertex AI (Workbench, Pipelines, Training, Model Registry, Online/Batch Prediction, Feature Store, Model Monitoring) and Gemini/LLM services—optimized for performance, cost, and maintainability.
- Develop production data pipelines with BigQuery, Dataflow, and Dataproc; integrate streaming via Pub/Sub; containerize and orchestrate with Cloud Run and GKE; automate CI/CD with Cloud Build and IaC.
- Implement robust MLOps (experiment tracking, evaluation, bias/robustness testing, model versioning, canary/blue‑green rollouts, automated retraining, drift detection, and lineage).
- Apply secure‑by‑design patterns—VPC‑SC, private service access, CMEK, fine‑grained IAM, artifact signing, and secrets management—aligned to NIST 800‑53, RMF, and FedRAMP baselines.
- Operationalize LLM/GenAI (RAG, tool‑use/agents, safety filters, evaluation harnesses) including retrieval over structured/unstructured data; leverage DoW‑approved AI toolchains where appropriate.
- Partner with mission stakeholders to elicit requirements, frame measurable success criteria, and deliver iterative value; provide technical mentorship and lead design/code reviews for engineering teams.
- Contribute to program roadmaps (including use cases), documenting architectures, controls, and SOPs for sustained operations.