• Define, design, and supervise the technical architecture of solutions based on intelligent agents and LLMs, integrating tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent frameworks. • Implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) architectures to enable multi-agent coordination and autonomous flows within business environments. • Work with the MLOps team and execution environments that enable continuous agent updating and deployment, including memory management, context, and long-term planning. • Collaborate closely with product, UX, data, and backend teams to map business needs to intelligent agent architectures.

• Design, develop, test, and maintain full-stack web applications. • Develop backend APIs and services using modern software engineering practices. • Build responsive frontend interfaces using contemporary JavaScript frameworks. • Utilize agentic AI development tools (e.g., Claude Code, Codex, GitHub Copilot, Cursor, or similar) to accelerate software development while maintaining high code quality. • Design and implement AI-powered workflows, automation, and intelligent agents. • Contribute to cloud-native application deployment and infrastructure. • Assist with containerization, CI/CD pipelines, and DevOps automation. • Support AI infrastructure, including model deployment, vector databases, retrieval systems, and GPU-enabled workloads. • Participate in software testing, quality assurance, and code reviews. • Collaborate with cybersecurity researchers to integrate AI into real-world security applications. • Document software designs, APIs, and engineering processes.

• Embed with a functional department for a defined engagement, running discovery to understand workflows and pain points before writing a line of code. • Scope, design, and build full-stack custom applications (not just automations or scripts) using modern frameworks, AI coding assistants, and internal platform APIs, moving from prototype to production quickly. • Own delivery end to end including architecture, build, testing, deployment, and the training and handoff that gets a department to actually adopt what you built. • Work hands-on in the code alongside AI coding tools (Claude Code, Codex, etc.) to compress build cycles, iterating with stakeholders in near real time rather than through long requirements documents. • Make explicit trade-offs between scope, speed, and quality as requirements shift, and communicate those trade-offs clearly to stakeholders and leadership. • Partner with IT, Security, and Data teams so every deployed application meets SentinelOne's standards for data security, compliance, and long-term supportability. • Codify recurring patterns, components, and playbooks from each engagement into reusable building blocks for the next department and for the broader engineering organization. • Feed field learnings back to platform, product, and data teams covering what worked, what broke, and what the next generation of internal tools should look like. • Measure and report success through production adoption and measurable business impact, not lines of code shipped or tickets closed.

• Embed with a functional department for a defined engagement, running discovery to understand workflows and pain points before writing a line of code. • Scope, design, and build full-stack custom applications (not just automations or scripts) using modern frameworks, AI coding assistants, and internal platform APIs, moving from prototype to production quickly. • Own delivery end to end including architecture, build, testing, deployment, and the training and handoff that gets a department to actually adopt what you built. • Work hands-on in the code alongside AI coding tools (Claude Code, Codex, etc.) to compress build cycles, iterating with stakeholders in near real time rather than through long requirements documents. • Make explicit trade-offs between scope, speed, and quality as requirements shift, and communicate those trade-offs clearly to stakeholders and leadership. • Partner with IT, Security, and Data teams so every deployed application meets SentinelOne's standards for data security, compliance, and long-term supportability. • Codify recurring patterns, components, and playbooks from each engagement into reusable building blocks for the next department and for the broader engineering organization. • Feed field learnings back to platform, product, and data teams covering what worked, what broke, and what the next generation of internal tools should look like. • Measure and report success through production adoption and measurable business impact, not lines of code shipped or tickets closed.