AI Security Developers (Python)

Roles & Responsibilities

• Integrate vendor AI security solutions with the firm's enterprise infrastructure, platforms, APIs, identity systems, and deployment pipelines.

• Deploy, configure, and support LLM inference frameworks on Microsoft Azure or other cloud environments, ensuring scalability, reliability, security, and operational readiness.

• Develop Python-based services, APIs, automation scripts, integration components, and tooling to support AI security and LLM platform capabilities.

• Build solutions that enhance the firm's security posture across LLM applications, AI agents, developer workflows, and enterprise AI infrastructure.

• Collaborate with security, infrastructure, platform engineering, cloud, and application development teams to deliver secure and production-ready AI capabilities.

• Work closely with teammates to design technical solutions, review implementation approaches, troubleshoot issues, and support delivery milestones.

Required Skills

• Strong programming skills in Python.

• Hands-on experience developing AI agents or LLM-powered applications.

• Familiarity with agent development frameworks such as:

  • OpenAI Agents SDK

  • Google Agent Development Kit

  • LangGraph

  • Similar agent orchestration frameworks

• Familiarity with AI coding assistants such as Claude Code, GitHub Copilot, or equivalent tools.

• Understanding of core LLM concepts, AI agent concepts, and agentic workflow patterns.

• Basic understanding of Agentic AI security, LLM security, and common AI security risks. Familiarity with OWASP AI/LLM threats, including prompt injection, sensitive information disclosure, insecure plugin or tool use, excessive agency, and model misuse.

• Familiarity with the concepts of AI gateways and MCP gateways.

• Experience with OpenTelemetry or similar logging, tracing, and observability tools.

• Good knowledge of Linux, shell scripting, Git, Docker, and Kubernetes.

• Experience working with APIs, microservices, CI/CD pipelines, and cloud or containerized environments.

• Ability to work with security, infrastructure, and application teams to deliver secure enterprise AI capabilities.