Data Engineer at Credence

McLean, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

30 Mar, 26

Salary

0.0

Posted On

30 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Integrations, Data Lake Engineering, Database Management, Collaborative Engineering, Cloud Enablement, Production Monitoring, Technical Rigor, Python Proficiency, IaC Tools, Generative AI, AI APIs, CI/CD Pipelines, Communication Skills, Client-Oriented Mindset, Data Pipeline Processes, Vector Databases, Agentic Frameworks

Industry

Description

Overview 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. Position Summary 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. Responsibilities include, but are not limited to the duties listed below 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. 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. Preferred 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. Why This Role Matters Real-World Impact — Your work will support defense and health agencies where AI solutions directly contribute to national security and public well-being. Growth-Oriented Environment — Learn from technical leaders, innovate within Agentic AI, and mentor those around you in AI best practices. Culture of Empowerment — You’ll be part of a team that values innovation, trust, collaboration, and mission success. Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Life Insurance (Basic, Voluntary & AD&D) Paid Time Off (Vacation, Sick & Public Holidays) Family Leave (Maternity, Paternity) Short Term & Long Term Disability Training & Development Free Food & Snacks Wellness Resources

Responsibilities

The Data Engineer will build and optimize data pipelines for generative AI and LLM usage, manage large datasets across cloud platforms, and collaborate with engineering and data teams to develop AI systems. Responsibilities also include monitoring AI systems post-deployment and ensuring code quality and documentation.