Senior AI Engineer

Responsibilities

Build Agentic & Intelligent AI Systems

• Design and implement LLM-powered systems capable of multi-step reasoning, evidence grounding, and decision support in high-trust domains.
• Develop agentic workflows that combine retrieval, tool use, structured reasoning, and human oversight.
• Create interactive AI experiences that allow users to engage naturally with complex compliance and risk data.

Automated Reasoning Over Regulations & Evidence

• Build AI systems that reason over structured and unstructured data to support regulatory interpretation, control alignment, and evidence validation.
• Ensure AI outputs are traceable, explainable, and auditable, meeting the expectations of enterprise customers and auditors.

Production-Grade AI Architecture

• Architect and deploy scalable LLM + retrieval + agent systems in production environments.
• Optimize for latency, cost, reliability, and evaluation in real-world enterprise workloads.
• Partner with platform, security, product teams, and other application development teams with diverse skill sets to operationalize AI safely and effectively.

Responsible & Trustworthy AI

• Embed human-in-the-loop workflows, confidence thresholds, and safety guardrails into AI systems.
• Ensure privacy-preserving data handling, robust failure modes, and transparent behavior aligned with Drata’s trust principles.

Requirements

• 7+ years of hands-on software engineering experience; 2+ years specifically in ML/AI engineering.
• Proficiency in Python; TypeScript experience is a plus, especially for production AI system integrations.
• Familiarity with vector databases (Pinecone, Chroma, FAISS, etc.) and RAG system design.
• Proven experience building and shipping LLM-based applications in production, including embeddings, RAG, agent frameworks, prompt engineering, and evaluation.
• Track record of taking AI systems from concept to production, designing scalable, maintainable solutions.
• Ability to decompose complex tasks into agentic workflows and reason over high-stakes, structured, and unstructured data.
• Experience working cross-functionally with product, compliance, security, engineering teams, and partnering with other application development teams with diverse skill sets.

Nice-to-Haves

• Experience in compliance, security, risk, or audit domains.
• Familiarity with Snowflake-based analytics, knowledge graphs, or enterprise data platforms.
• Experience partnering with non-technical stakeholders such as compliance or GRC teams.