Senior Google AI Engineer

Credence•McLean, VA

About The Position

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. 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 Defense 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 DoD 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.

Requirements

US citizenship with the ability to obtain successful DoD secret security clearance required. Candidates with active secret clearance preferred.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.
8+ years total software development/data experience with 5+ years focused on AI/ML engineering and MLOps, including production deployments on Google Cloud (GCP).
Expert in Python (and/or Go/TypeScript) for AI services; strong with Vertex AI, BigQuery, Dataflow, GKE/Cloud Run, Cloud Build, Cloud Storage, Pub/Sub.
Practical experience with LLMs/GenAI (e.g., Gemini), vector databases, prompt engineering, RAG patterns, and evaluation/guardrails.
Proven MLOps track record (Pipelines, CI/CD for ML, feature stores, monitoring/drift, automated retraining) and strong data engineering fundamentals.
Ability to design for security & compliance in DoD/Federal contexts (NIST 800‑53, RMF, FedRAMP; Zero Trust principles).

Nice To Haves

Advanced degree (Master’s or PhD) in AI/ML, Data Science, Computer Science, or a closely related discipline is preferred but not required, especially when balanced with substantial hands‑on experience (5+ years) in AI/ML solution development.
Hands‑on with Vertex AI Agent Builder, Model Garden, embeddings/vector search (e.g., BigQuery Vector, AlloyDB AI), and evaluation frameworks.
Experience integrating GenAI safely within IL‑5 environments and familiarity with available government AI platforms

Responsibilities

Architect & 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 DoD‑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.