Responsibilities
- Design, develop, and maintain Python-based data processing pipelines and workflow orchestration solutions for large-scale text ingestion, transformation, and enrichment.
- Develop and implement AI-powered agentic workflows and LLM-integrated applications to automate data triage, classification, analysis, and processing tasks.
- Build, enhance, and maintain reusable AI capabilities, prompt frameworks, and agent-based services that support enterprise data analysis platforms.
- Integrate and operationalize Large Language Models (LLMs) to deliver retrieval, reasoning, summarization, information extraction, and decision-support capabilities.
- Troubleshoot, optimize, and scale text-processing pipelines to ensure data quality, system reliability, and efficient AI-driven workflows.
- Design and develop APIs and backend services that connect AI models, data pipelines, and mission applications.
- Collaborate with cross-functional teams including data scientists, software engineers, and product stakeholders to prototype, test, and deploy AI-enabled solutions.
- Develop Python-based automation tools and services supporting data engineering, workflow orchestration, model integration, and operational efficiencies.
- Support the deployment and maintenance of production-scale AI and machine learning solutions in mission-focused environments.
- Evaluate emerging AI technologies and recommend enhancements that improve analytical capabilities and operational outcomes.
Basic qualifications
- Active TS/SCI with Polygraph Clearance
- Develop and perform ETL on large unstructured datasets.
- Experience with Python
- Experience with services including Apache Kafka, Apache Spark, and Prefect
- Experience containerizing applications using Docker and deployments on Kubernetes
- Building and maintaining CI/CD pipelines for data and platform services
- Familiarity with Linux-based systems
- Solid understanding of DevOps principles (automation, monitoring, reliability)
Tags & focus areas
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