Senior Google AI Engineer
New
C
CredenceAI/ML engineering
Workable workplace: remote; Location: McLean, Virginia, United StatesFull-TimeSenior
Salary150,000 - 190,000 USD per year
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Job Details
- Experience
- 8+ years total software development/data experience with 5+ years focused on AI/ML engineering and MLOps
- Required Skills
- PythonBigQueryLookerMLOps
Requirements
- Hold a bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.
- Have 8+ years of total software development/data experience, including 5+ years focused on AI/ML engineering and MLOps.
- Have experience deploying AI/ML solutions in production on Google Cloud.
- Have experience with data ingestion and processing using Dataflow, Data Fusion, Dataproc, BigQuery, and Looker, integrated with Vertex AI.
- Hold a current Google Associate Cloud Engineer (GCP-ACE) or Google Professional Cloud Architect (GCP-PCA) certification, or obtain one within 90 days of hire.
- Hold a current DoW Cyber Baseline Certification, such as Security+ Intermediate or SecurityX/CASP+ Advanced, or obtain one within 90 days of hire.
- Have experience extracting data from SAP Enterprise Business Applications.
- Have expertise in Python and/or Go/TypeScript and strong experience with Vertex AI, BigQuery, Dataflow and/or Data Fusion, GKE/Cloud Run, Cloud Build, Cloud Storage, and Pub/Sub.
- Have practical experience with LLMs/GenAI, vector databases, prompt engineering, RAG, evaluation, and guardrails.
- Have a proven MLOps track record, including pipelines, ML CI/CD, feature stores, monitoring/drift, and automated retraining.
- Have experience with Vertex AI Agent Builder, Model Garden, embeddings/vector search, and evaluation frameworks.
- Have experience integrating GenAI safely in IL5 environments and familiarity with government AI platforms.
- Be able to design for security and compliance in DoW/Federal contexts, including NIST 800-53, RMF, FedRAMP, and Zero Trust principles.
Responsibilities
- Architect and deliver end-to-end AI/ML solutions on Google Cloud using Vertex AI and Gemini/LLM services.
- Develop production data pipelines with BigQuery, Dataflow, and Dataproc; integrate Pub/Sub streaming and containerize and orchestrate with Cloud Run and GKE.
- Automate CI/CD with Cloud Build and infrastructure as code.
- Implement MLOps practices including experiment tracking, evaluation, model versioning, automated retraining, drift detection, and lineage.
- Apply secure-by-design patterns aligned to NIST 800-53, RMF, and FedRAMP baselines.
- Operationalize LLM/GenAI, including RAG, agents, safety filters, and evaluation harnesses.
- Partner with mission stakeholders to elicit requirements, define success criteria, and deliver iterative value.
- Provide technical mentorship, lead design and code reviews, and document architectures, controls, and SOPs.
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