Senior Google AI Engineer
Credence Management Solutions, LLC5 days ago
Remote, United States or McLean, VA, USASenior
Base Salary
$150k - $190k/yr
Responsibilities
- Architect and deliver end-to-end AI/ML systems on Google Cloud using Vertex AI, Gemini, and related model training, deployment, prediction, feature management, and monitoring services.
- Develop production data pipelines with BigQuery, Dataflow, Dataproc, Data Fusion, Pub/Sub, Cloud Run, GKE, Cloud Storage, and Cloud Build.
- Implement MLOps practices including experiment tracking, evaluation, bias and robustness testing, model versioning, rollout strategies, retraining, drift detection, and lineage.
- Apply secure-by-design architecture using VPC Service Controls, private service access, CMEK, IAM, artifact signing, secrets management, and federal security frameworks.
- Operationalize LLM and GenAI solutions using RAG, tool-use and agents, safety filters, evaluation harnesses, structured and unstructured data retrieval, and vector search.
- Partner with mission stakeholders, define measurable success criteria, mentor engineering teams, lead design and code reviews, and contribute to roadmaps, architecture documentation, controls, and SOPs.
Requirements
- US citizenship and the ability to obtain a DoW Secret security clearance are required; an active Secret clearance is preferred.
- Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field is required.
- 8+ years of total software development or data experience, including 5+ years focused on AI/ML engineering and MLOps with production Google Cloud deployments.
- Experience architecting data ingestion, processing, and transformation workflows using Dataflow, Data Fusion, Dataproc, BigQuery, Looker, and Vertex AI.
- Current Google Associate Cloud Engineer or Google Professional Cloud Architect certification, or ability to obtain it within 90 days of hire.
- Current DoW Cyber Baseline Certification, such as Security+ Intermediate or SecurityX/CASP+ Advanced, or ability to obtain it within 90 days of hire.
- Experience extracting data from SAP Enterprise Business Applications.
- Expertise in Python and/or Go or TypeScript, with strong experience in Vertex AI, BigQuery, Dataflow or Data Fusion, GKE or Cloud Run, Cloud Build, Cloud Storage, and Pub/Sub.
- Practical experience with LLMs and GenAI, including Gemini, vector databases, prompt engineering, RAG patterns, evaluation, and guardrails.
- Proven MLOps experience with pipelines, ML CI/CD, feature stores, monitoring and drift detection, automated retraining, and strong data engineering fundamentals.
- Ability to design secure and compliant solutions for DoW and federal environments using NIST 800-53, RMF, FedRAMP, and Zero Trust principles.
- Hands-on experience with Vertex AI Agent Builder, Model Garden, embeddings and vector search such as BigQuery Vector and AlloyDB AI, evaluation frameworks, and IL5 GenAI integrations.
- An advanced degree in AI/ML, Data Science, Computer Science, or a related discipline is preferred but not required.
Benefits
- Remote full-time work arrangement.
- Health care plan covering medical, dental, and vision.
- Retirement plan options including 401(k) and IRA.
- Basic, voluntary, and AD&D life insurance.
- Paid vacation, sick leave, and public holidays.
- Maternity and paternity family leave.
- Short-term and long-term disability coverage.
- Training and development opportunities.
- Wellness resources.
Tech Stack
Categories
Data EngineeringML Engineering