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RADICL

Senior Engineer - AI-Driven Workflows

AI Application Engineering +1Updated on Nov 15
Full-timeBoulder, CO, USA

About the Job

Requirements

  • At least 3 years of experience implementing production AI workflows, preferably involving large language models, agent systems, or intelligent automation platforms.
  • Hands-on experience with GPT, Claude, or similar models, including prompt engineering, fine-tuning, and AI safety considerations.
  • Strong background designing reliable, scalable event-driven systems using Kafka, stream processing, message queues, and asynchronous workflows.
  • Proficiency in Go for backend services and Python for AI/ML pipelines; familiarity with LangChain, LlamaIndex, or similar frameworks.
  • Understanding of automated incident response workflows, entity relationship modeling, threat attribution methodologies, and cybersecurity operations.
  • Experience with Elasticsearch or OpenSearch, PostgreSQL, and large-scale security data processing and analysis.
  • Ability to build reliable, testable systems incorporating AI feedback while maintaining predictable behavior and auditability.
  • Experience designing RESTful APIs and real-time interfaces such as WebSockets and Server-Sent Events for automated AI workflow systems and data processing pipelines.
  • AWS experience, particularly with Bedrock, Lambda, ECS, and other AI/ML services.
  • Understanding of business process automation, rule engines, and configurable, repeatable workflows.
  • Experience testing AI-powered applications, including prompt testing, model validation, and regression detection.

Responsibilities

  • Design and build intelligent case management systems that automatically triage security incidents, generate threat context reports, and execute response actions without human intervention in the critical path.
  • Develop agent systems using large language models that autonomously reason about security data, correlate threat patterns, and execute investigation workflows.
  • Create reliable, event-driven automation systems that use AI feedback to improve decision-making, automate response actions, and process incidents consistently at scale.
  • Build systems that consume security data feeds, enrich indicators of compromise, correlate threat intelligence, and generate actionable insights automatically.
  • Develop systems correlating structured security data, unstructured threat intelligence, and analyst feedback to improve AI model performance and workflow reliability.
  • Implement Kafka-based event-driven architectures to process security alerts, enrich threat data, and trigger AI-driven responses in real time.
  • Integrate AWS Bedrock, Azure OpenAI, and other AI platforms; optimize model selection, prompt engineering, and cost-effective AI operations at scale.

Desired Qualifications

  • MLOps experience with model deployment, monitoring, and lifecycle management.
  • SOAR platform experience, such as Phantom or Demisto, or a security orchestration background.
  • Threat intelligence experience with automated indicator-of-compromise enrichment, entity correlation, MISP, STIX/TAXII, or threat attribution analytics.
  • React and TypeScript experience for building AI-powered user interfaces.
  • Experience with vector databases and semantic search for security data correlation.
  • Experience with graph databases or knowledge representation for threat modeling.

About the Company

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