Mid-Level AI Engineer

Credence•McLean, VA

3d•Remote

About The Position

Credence has an immediate need for a Mid-Level AI Engineer to join our growing AI and Automation practice. You will be a technical anchor in our AI and Automation practice. You’ll apply foundational AI/ML skills to build and deploy data-driven solutions. Under mentorship from senior AI leaders, you’ll drive model development lifecycles and collaborate across engineering, data, and stakeholder teams to deliver high-impact, cloud-native AI capabilities that advance federal missions.

Requirements

U.S. citizenship with the ability to obtain a public trust.
Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or a related field.
3–5 years of hands-on experience delivering AI/ML solutions.
Familiarity with OpenAI, Claude Sonnet, Claude Opus, and other AI engines.
Strong Python proficiency and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Understanding of supervised and unsupervised learning techniques.
Experience working with cloud platforms (AWS, Azure, or GCP) and container tools (Docker, Kubernetes).
Familiarity with CI/CD pipelines and basic MLOps workflows.
Experience or interest in generative AI and working with LLMs.
Experience with VS Code and AI extensions like Cline and Claude Code, in addition to QDeveloper.
Strong communication skills and client-oriented mindset.

Nice To Haves

Experience with Bedrock, Strands, Q Developer, Kiro, AgentCore Gateway.
Exposure to AWS Serverless technologies including ones that enable Event based architecture (important for Agentic AI systems).
Experience with IaC tools (AWS CDK, Terraform, CloudFormation) or model monitoring tools.
Exposure to data engineering concepts or data pipeline optimization.
Knowledge of federal cybersecurity, RMF, FedRAMP, or regulatory frameworks.
Knowledge of MCP servers and how they can be used for re-usable archetypes between teams.

Responsibilities

Support end-to-end AI development: data prep, feature engineering, model training, evaluation, and integration into production pipelines.
Work alongside data engineers, software engineers, and data scientists to embed AI into scalable systems and applications.
Help automate model deployment workflows using Infrastructure as Code (IaC), CI/CD pipelines, and container orchestration tools.
Contribute to projects using generative AI and LLMs, helping to prototype, customize, and refine models.
Monitor AI systems post-deployment, perform performance tuning, and apply best practices for reliability and scalability.
Write clean, well-documented code following industry and federal guidelines; support reproducible development.
Stay current on AI/ML trends and tools, and actively learn from senior team members through mentorship and technical design reviews.