AI Forward Deployed Engineer - UAE

About the Role

Core Customer Solution Work

• Understand customer problems and operational environments, then select the appropriate agents, tools, integrations, and data.
• Design agentic workflows, build missing components in Python, connect agents to customer technologies, and test and deploy solutions.
• Convert successful customer-specific implementations into reusable platform building blocks.

Build and Deploy Agentic AI

• Design and implement production-grade AI agents and multi-agent workflows.
• Implement orchestration, routing, planning, reasoning, tool use, memory, state management, and human-in-the-loop workflows.
• Optimize AI workflows for alert triage, investigation, threat hunting, incident response, detection engineering, forensics, and security automation.

Forward Deployment

• Deploy Autonomous SOC capabilities in enterprise and MSSP environments.
• Customize agents and build customer-specific tools, integrations, prompts, workflows, and agent graphs.
• Troubleshoot production behavior, integrate customer systems, and take requirements back to core engineering.

Technical Requirements

• Excellent hands-on Python knowledge is mandatory, including APIs, SDK development, data processing, testing, debugging, authentication, error handling, and observability.
• Required agentic AI knowledge includes LangChain, LangGraph, tool calling, multi-agent architectures, state and memory, orchestration, evaluation, guardrails, and prompt or context engineering.
• LLM engineering topics include compatible APIs, open-source models, embeddings, RAG, vector search, context management, structured generation, evaluation, and benchmarking.
• Infrastructure knowledge includes REST or GraphQL APIs, webhooks, OAuth2, event-driven systems, permissions, Docker, Kubernetes, Linux, Git, CI/CD, and cloud platforms.
• Cybersecurity knowledge should include SOC operations, SIEM, EDR/XDR, SOAR, detection engineering, incident response, threat hunting, DFIR, MITRE ATT&CK, Sigma, and security telemetry.

What Makes a Great Candidate

• Has built real LLM or agentic AI applications and can rapidly prototype while writing production-quality code.
• Can independently debug unfamiliar environments and solve difficult problems directly with customers.
• Communicates clearly with engineers and security teams and understands safe interaction between AI agents and real systems.

Nice to Have

• Experience with autonomous or multi-agent systems, cybersecurity products, SOC/SIEM/SOAR platforms, MCP servers, local or air-gapped LLMs, RAG, observability, or model distillation is advantageous.
• Experience with enterprise SaaS, MSSP or multi-tenant environments, customer-facing engineering, or difficult undocumented APIs is advantageous.

Workplace

• The offer describes a flexible remote setup.

Compensation

• The compensation package combines a competitive base salary and restricted stock units.
• The offer includes hardware and a budget for AI tools, courses, conferences, and model API credits.