Dexian DISYS
AI and Data Engineer
Washington, District of Columbia, USA · full-time
Sponsorship not specified$180k-$180kDetected 43 days ago
PythonCode ReviewSQLSnowflakeDatabricksVector DatabasesAWSCI/CDPlatform EngineeringMachine LearningSparkdbtData EngineeringData ScienceLLMsRAGTest AutomationCollaborationPipeline Integrity
About the role
- The Data & AI Engineer is an experienced, hands-on engineer who turns data and AI architecture into working production systems.
- The role requires deep, hands-on expertise across modern data engineering and applied AI engineering.
- Contribute to semantic and context engineering work that powers natural-language analytics, conversational reporting, and AI-driven insights for business users.
Responsibilities
- AI Data Pipelines & Retrieval Systems (?35%) Build and operate AI-ready data pipelines - embedding generation, chunking, indexing, and refresh workflows - that make enterprise data reliably retrievable by LLMs, agents, and generative AI applications.
- Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks.
- Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products.
- Modern Data Pipeline Engineering (?30%) Design, build, and maintain production-grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake.
- Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency.
- Write clean, well-tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing.
- Semantic Layer & Data Product Development (?20%) Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self-service analytics and reliable AI grounding.
- Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers.
- Collaborate with the Senior AI & Data Architect to refine and extend enterprise semantic standards based on what works in production.
- Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction.
Requirements
- Proven experience implementing retrieval, grounding, and semantic components for LLM- or agent-based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use.
- Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark).
- Deep, hands-on experience with modern data stacks - dbt, Fivetran, Snowflake - in AWS-based environments.
Nice to have
- Palantir experience a plus.
- Experience operating within federated data operating models and complex, regulated enterprise environments
- financial services experience preferred.
Skills
- To learn more, please visit.
Compensation
- $180k-$180k
Visa & Work Authorization
- candidates without regard to race, religion, sex, sexual orientation, gender identity, age, national origin, ancestry, citizenship, disability, or veteran status
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