Data 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. Credence has an immediate need for a Mid-Level AI Data 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 skills to build and deploy data-driven solutions. Under mentorship from senior AI leaders, you’ll drive agentic AI 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 eligibility for DoD Secret clearance.
Bachelor’s or Master’s in Computer Science, Data/AI/ML, or a related field.
3–7 years of hands-on experience delivering Data/AI/ML solutions.
Strong understanding of ETL, ELT, and other similar data pipeline processes, as well as Enterprise Data and Storage Systems, such as experience with OpenSearch/Elastic Search, Kafka, Bedrock, Glue, DataBricks, Snowflake, AWS S3, RDS, EBS, or Glacier
Experience with vector databases, embeddings, and their affiliated data structures, file formats, services, APIs, etc (e.g. FAISS, PGVector, OpenSearch/Elasticsearch, Hugging face with Pinecone, Bedrock Knowledge Base)
Experience in generative AI, working with LLMs, adding tool calls (MCP) and agents (A2A).
Understanding of leading AI APIs such as OpenAI, Anthropic, Gemini, Bedrock, Vertex for use with LLMs and RAG search.
Familiarity with CI/CD pipelines (GitLab/GitHub/Jenkins)
Experience with VS Code and AI extensions such as Cline and Claude Code.
Strong communication skills and client-oriented mindset.

Nice To Haves

Curious and experimental about the latest innovations in AI with an orientation toward the relentless pursuit of delivering mission impact.
Python proficiency and familiarity with libraries and frameworks (Pyspark, Pandas, uv, Pydantic, FastAPI, CrewAI, LangChain, LangGraph, Unstructured).
Experience with IaC tools such as Terraform, Open Tofu, AWS CDK, or CloudFormation to deploy cloud native applications.
Experience with agentic frameworks such as Agent2Agent Protocol, AWS Bedrock Agents, Mastra, CrewAI, Strands, or AgentCore.
Exposure to adjacent skillsets such as data science, UI/UX, cloud engineering, and platform engineering to understand the entire software ecosystem.
Knowledge of federal cybersecurity, RMF, FedRAMP, or regulatory frameworks.

Responsibilities

Data Integrations for Generative AI & LLM Usage
Build and optimize data pipelines that prepare, clean, and structure data for generative AI and LLM usage.
Data Lake & Warehouse Engineering
Manage and organize large datasets across cloud platforms (e.g., AWS, Azure, GCP) using data lake and warehouse technologies. Implement medallion architecture (Bronze/Silver/Gold layers) to ensure data quality, lineage, and accessibility.
Database Management & Performance
Work with both SQL and NoSQL systems to model, query, and load large-scale datasets. Monitor, tune, and maintain high-performance data stores supporting analytics and reporting.
Collaborative Engineering
Work alongside data engineers, software engineers, and data scientists to develop operational agentic AI systems.
Cloud Enablement
Help automate model deployment workflows using Infrastructure as Code (IaC), CI/CD pipelines, and container orchestration tools.
Production Monitoring & Optimization
Monitor AI systems post-deployment, 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.
Professional Growth
Stay current on AI/ML trends and tools and actively learn from senior team members through mentorship and technical design reviews.