Senior AI Engineer

About the Role:

We are looking for a Senior AI Engineer with deep experience building LLM-powered agents to join our AI

Engineering team. You will design and ship agentic systems that reason over data security signals, automate

investigation and recovery workflows, and surface insights to our customers and internal teams. This is a hands-on

engineering role for someone who has taken GenAI agents from prototype to production and can own systems end to

End.

What You’ll Do

Build agents: Design, build, and deploy production-grade LLM/GenAI agents — including multi-step

reasoning, tool use, orchestration, and memory — that operate reliably at enterprise scale.

Engineer in Python: Develop and maintain agent frameworks and pipelines in Python, integrating LLMs with

internal tools, APIs, and data sources.

Configure with YAML: Author and manage YAML-based configurations for agent behaviors, prompts,

workflows, and deployment pipelines.

Ensure quality: Establish evaluation, guardrails, observability, and testing practices to ensure agent accuracy,

safety, and cost-efficiency.

Collaborate: Partner with product, security research, and platform teams to translate requirements into robust

agentic solutions.

Lead: Mentor engineers, drive technical design reviews, and set best practices for agent development across the

Org.

What You’ll Bring

Experience: 7–10 years of software / AI engineering experience, with a strong track record of shipping systems

to production.

Python: Expert-level Python — clean, well-tested, production-quality code and strong grasp of software

design.

Agent building: Hands-on experience building LLM/GenAI agents: agent frameworks (e.g., LangChain,

LangGraph, CrewAI, or similar), tool/function calling, RAG, and prompt engineering.

YAML: Proficiency with YAML for configuration, workflow definition, and infrastructure/deployment

tooling.

LLMs: Experience with LLM APIs and models (e.g., Anthropic Claude, OpenAI, open-source models) and

understanding of their trade-offs.

Rigor: Solid understanding of evaluation, observability, and safety/guardrails for agentic systems.

Cloud & infra: Familiarity with cloud platforms (AWS/Azure/GCP), containers, and CI/CD.

Nice to Have

Domain: Experience in data security, cybersecurity, or data management domains.

OSS: Contributions to open-source agent or ML tooling.

Retrieval: Experience with vector databases, embeddings, and retrieval systems.

Scale: Background in MLOps or distributed systems.