About the job
Become a pivotal member of a team where cutting-edge innovation aligns with vital missions. At Credence, we specialize in AI, cloud, cyber, and modernization solutions that empower agencies, enhance national security, and bolster health and humanitarian efforts worldwide. Our talented workforce of 1,700+ includes over 1,500 AI and data specialists, supporting more than 100 prime contracts, allowing us to deliver impactful solutions at scale.
For six consecutive years, we've been honored as a Top Workplace by the Washington Post and recognized in the Inc. 5000 list of Fastest Growing Private Companies for 13 of the past 14 years. At Credence, we embrace individuals eager to grow professionally and make a meaningful impact, encouraging our team to tackle significant Federal challenges.
Position Summary
We are seeking a highly qualified Senior Google AI Engineer to enhance our Google Cloud AI engineering capabilities in support of Department of Defense (DoD) programs. You will be responsible for designing, building, and operationalizing production-grade AI systems on Google Cloud to accelerate mission outcomes for a high-visibility program.
In this role, you will take on a hands-on technical leadership position for AI solution delivery, translating mission requirements into secure, scalable AI/ML systems. You will guide data, platform, and application engineers, ensuring all solutions adhere to DoD security and compliance standards in production. The ideal candidate possesses extensive expertise in GCP, Looker, BigQuery, Vertex AI, alongside robust MLOps and data engineering skills, with experience in regulated environments.
Key Responsibilities
- Design and deliver comprehensive end-to-end AI/ML solutions on Google Cloud utilizing Vertex AI (Workbench, Pipelines, Training, Model Registry, Online/Batch Prediction, Feature Store, Model Monitoring) and Gemini/LLM services, with a focus on performance, cost-efficiency, and maintainability.
- Create production data pipelines using BigQuery, Dataflow, and Dataproc; implement streaming integration via Pub/Sub; containerize and orchestrate applications with Cloud Run and GKE; and automate CI/CD processes with Cloud Build and Infrastructure as Code.
- Establish robust MLOps practices including experiment tracking, evaluation, bias testing, model versioning, canary/blue-green rollouts, automated retraining, drift detection, and lineage.
- Utilize secure-by-design patterns, VPC-SC, private service access, CMEK, fine-grained IAM, artifact signing, and secrets management, aligned with NIST 800-53, RMF, and FedRAMP baselines.
- Operationalize LLM/GenAI (RAG, tool-use agents, safety filters, evaluation harnesses) focusing on retrieval from structured and unstructured data, employing DoD-approved AI toolchains where applicable.
- Collaborate with mission stakeholders to gather requirements and define measurable success criteria.
