Credence company
Mid-Level Google AI/Data Engineer
Credence company, Falls Church, Virginia, United States, 22042
Mid-Level Google Ai/Data Engineer
Credence supports our clients' mission-critical needs, powered by technology. We provide cutting-edge solutions, including AI/ML, enterprise modernization, and advanced intelligence capabilities, to the largest defense and health federal organizations. Through partnership and trust, we increase mission success for war-fighters and secure our nation for a better future. Credence has an immediate need for a Mid-Level Google AI/Data Engineer to join our growing AI and Automation practice. You will be a technical anchor in our AI and Automation practice, focused on leveraging Google Cloud Platform (GCP) native services. You'll apply foundational AI/ML and data engineering skills to build and deploy cloud-first, data-driven solutions. Under mentorship from senior AI leaders, you'll drive data pipeline engineering, model development lifecycles, and collaborate across engineering, data, and stakeholder teams to deliver high-impact, cloud-native AI capabilities that advance federal missions. Responsibilities Include, But Are Not Limited To The Duties Listed Below
AI Model Development & Integration Support end-to-end AI development: data prep, feature engineering, model training, evaluation, and integration into production pipelines using GCP AI/ML services. Data Engineering & Pipeline Enablement Design, build, and optimize scalable data pipelines using BigQuery, Dataflow, and Pub/Sub. Enable structured, high-quality datasets for downstream AI/ML and analytics workloads. Collaborative Engineering Work alongside software engineers, data scientists, and cloud engineers to embed AI into scalable GCP-based systems and applications. Cloud & MLOps Enablement Automate model deployment workflows using Vertex AI, Cloud Build, IaC (Terraform, Deployment Manager), and Kubernetes (GKE). Generative AI & LLM Usage Contribute to projects using Google's Generative AI Studio, Vertex AI Search, and LLM APIs to prototype, customize, and refine models. Production Monitoring & Optimization Monitor AI/data systems post-deployment with Cloud Monitoring and Logging; perform performance tuning and apply best practices for reliability and scalability. Technical Rigor & Documentation Write clean, well-documented code following industry and federal guidelines; support reproducible development practices. Professional Growth Stay current on Google AI/ML innovations and tools, and actively learn from senior team members through mentorship and technical design reviews. Education, Requirements And Qualifications
What You Bring Bachelor's or Master's in Computer Science, AI/ML, Data Science, or a related field. 35 years of hands-on experience delivering AI/ML or data engineering solutions. Strong proficiency with Python and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Strong knowledge of Google Cloud services: BigQuery, Dataflow, Pub/Sub, Vertex AI, Cloud Functions, Cloud Storage. Understanding of supervised and unsupervised learning techniques. Experience with Kubernetes (GKE), Docker, and CI/CD workflows using Cloud Build. Familiarity with MLOps practices and model lifecycle management in GCP. Experience or interest in generative AI and working with LLMs in Google's AI ecosystem. Experience with VS Code, QDeveloper, and AI extensions like Cline or Claude Code. Strong communication skills and client-oriented mindset. U.S. Citizenship with eligibility for DoD Secret clearance. Preferred Experience with advanced GCP AI tools such as Generative AI Studio, Vertex AI Agent Builder, or Dataform. Exposure to event-driven and serverless architecture using Cloud Run, Cloud Functions, and Eventarc (important for agentic AI systems). Familiarity with IaC tools (Terraform, Deployment Manager, Google Cloud CLI). Exposure to data engineering concepts such as schema design, streaming analytics, or data pipeline optimization. Knowledge of federal cybersecurity, RMF, FedRAMP, or regulatory frameworks. Familiarity with multi-cloud environments and re-usable archetypes for federated data solutions Bachelor's or master's degree in business, management, engineering, marketing, or a field relevant to emerging technologies or Federal/DoD contracting. Must have excellent written and verbal communication skills. US Citizenship required with the possibility of obtaining a Federal Security Clearance or Active Clearance. Working Conditions And Physical Requirements
Please join us, as together we build a better world one mission at a time powered by Technology and its People!
Credence supports our clients' mission-critical needs, powered by technology. We provide cutting-edge solutions, including AI/ML, enterprise modernization, and advanced intelligence capabilities, to the largest defense and health federal organizations. Through partnership and trust, we increase mission success for war-fighters and secure our nation for a better future. Credence has an immediate need for a Mid-Level Google AI/Data Engineer to join our growing AI and Automation practice. You will be a technical anchor in our AI and Automation practice, focused on leveraging Google Cloud Platform (GCP) native services. You'll apply foundational AI/ML and data engineering skills to build and deploy cloud-first, data-driven solutions. Under mentorship from senior AI leaders, you'll drive data pipeline engineering, model development lifecycles, and collaborate across engineering, data, and stakeholder teams to deliver high-impact, cloud-native AI capabilities that advance federal missions. Responsibilities Include, But Are Not Limited To The Duties Listed Below
AI Model Development & Integration Support end-to-end AI development: data prep, feature engineering, model training, evaluation, and integration into production pipelines using GCP AI/ML services. Data Engineering & Pipeline Enablement Design, build, and optimize scalable data pipelines using BigQuery, Dataflow, and Pub/Sub. Enable structured, high-quality datasets for downstream AI/ML and analytics workloads. Collaborative Engineering Work alongside software engineers, data scientists, and cloud engineers to embed AI into scalable GCP-based systems and applications. Cloud & MLOps Enablement Automate model deployment workflows using Vertex AI, Cloud Build, IaC (Terraform, Deployment Manager), and Kubernetes (GKE). Generative AI & LLM Usage Contribute to projects using Google's Generative AI Studio, Vertex AI Search, and LLM APIs to prototype, customize, and refine models. Production Monitoring & Optimization Monitor AI/data systems post-deployment with Cloud Monitoring and Logging; perform performance tuning and apply best practices for reliability and scalability. Technical Rigor & Documentation Write clean, well-documented code following industry and federal guidelines; support reproducible development practices. Professional Growth Stay current on Google AI/ML innovations and tools, and actively learn from senior team members through mentorship and technical design reviews. Education, Requirements And Qualifications
What You Bring Bachelor's or Master's in Computer Science, AI/ML, Data Science, or a related field. 35 years of hands-on experience delivering AI/ML or data engineering solutions. Strong proficiency with Python and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Strong knowledge of Google Cloud services: BigQuery, Dataflow, Pub/Sub, Vertex AI, Cloud Functions, Cloud Storage. Understanding of supervised and unsupervised learning techniques. Experience with Kubernetes (GKE), Docker, and CI/CD workflows using Cloud Build. Familiarity with MLOps practices and model lifecycle management in GCP. Experience or interest in generative AI and working with LLMs in Google's AI ecosystem. Experience with VS Code, QDeveloper, and AI extensions like Cline or Claude Code. Strong communication skills and client-oriented mindset. U.S. Citizenship with eligibility for DoD Secret clearance. Preferred Experience with advanced GCP AI tools such as Generative AI Studio, Vertex AI Agent Builder, or Dataform. Exposure to event-driven and serverless architecture using Cloud Run, Cloud Functions, and Eventarc (important for agentic AI systems). Familiarity with IaC tools (Terraform, Deployment Manager, Google Cloud CLI). Exposure to data engineering concepts such as schema design, streaming analytics, or data pipeline optimization. Knowledge of federal cybersecurity, RMF, FedRAMP, or regulatory frameworks. Familiarity with multi-cloud environments and re-usable archetypes for federated data solutions Bachelor's or master's degree in business, management, engineering, marketing, or a field relevant to emerging technologies or Federal/DoD contracting. Must have excellent written and verbal communication skills. US Citizenship required with the possibility of obtaining a Federal Security Clearance or Active Clearance. Working Conditions And Physical Requirements
Please join us, as together we build a better world one mission at a time powered by Technology and its People!