Forward Deployment Engineer | AI Security Startup | LangChain, DLP, IAM, Python, Azure, LangGraph, OAuth, Docker, AI Agents, Cloud

About the Company

This is an AI security company operating at the cutting edge of enterprise AI, data protection, and identity controls — solving problems most companies haven’t even named yet. The product sits at the intersection of AI agents, DLP enforcement, and identity security, tackling real-world deployment challenges in cloud, hybrid, and on-prem environments ahead of full market maturity. A fast-moving, high-trust environment where engineers work directly inside customer environments, shape the product roadmap through field learnings, and help define what AI security actually looks like in production. This is where builders who thrive in ambiguity and high-stakes problem-solving make their mark.

Forward Deployment Engineer

A deeply technical, customer-facing Forward Deployment Engineer is needed to deploy, adapt, and operationalise AI and data security solutions directly inside enterprise customer environments. This is not a support role. Expect live deployments, real architectural decisions, high-stakes troubleshooting — and the satisfaction of making AI, DLP, and identity controls work under real-world constraints.

What You’ll Do

Deployment & Integration

• Deploy AI security solutions into cloud, hybrid, and on-prem customer environments
• Integrate AI agents and workflows using LangChain, LangGraph, and agent orchestration patterns (human + non-human identities)
• Integrate identity and access controls via Microsoft Entra ID (Azure AD), Okta, OAuth, token enforcement, and service identities
• Implement DLP inspection, policy enforcement, and audit logging flows

Hands-On Engineering & Troubleshooting

• Build custom scripts, connectors, and workflows using Python and Docker
• Troubleshoot production-grade issues across AI pipelines, identity flows, and data enforcement points
• Design practical workarounds when features are missing or incomplete
• Maintain deployment artifacts, scripts, and configurations in GitHub

Technical Storytelling & Customer Alignment

• Explain deployed architectures using clear technical narratives — how AI, identity, and data controls fit together and where enforcement boundaries lie
• Lead deep technical discussions on DLP realities — detection capabilities, structural limitations, and common misconceptions
• Help customers reason through risk trade-offs, enforcement gaps, and compensating controls without overselling

Workshops & Collaborative Problem Solving

• Co-lead technical workshops and whiteboarding sessions during deployments
• Collaborate with customer engineers to refine AI access paths, identity trust boundaries, and data protection strategies
• Surface environmental constraints early and drive cross-team alignment

Feedback & Product Influence

• Capture deployment learnings, edge cases, and failure modes
• Feed structured insights into Product and Engineering to influence roadmap and hardening
• Standardise successful deployment patterns for reuse across customers

What You Bring

• 12+ years in Deployment Engineering, Solutions Engineering, or Platform Engineering
• Hands-on experience with AI/LLM systems and AI security architectures
• Deep practical knowledge of DLP — classification, inspection, enforcement, limitations, and bypass scenarios
• Solid understanding of IAM — SSO, OAuth/OIDC, tokens, service identities, and policy engines
• Proficiency in Python, Docker, and cloud-native tooling — AWS, Azure, or GCP
• Familiarity with Microsoft Intune and Jamf Pro for endpoint and device trust integrations
• Strong debugging, problem-solving, and systems-thinking skills
• Ability to explain complex systems clearly to technical customer audiences
• Comfortable operating directly with customers in live, high-stakes environments

Education

• Relevant engineering degree or equivalent hands-on industry experience

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