Solutions engineer (ai, identity & data security)

About the Company

Solutions Engineer (AI, Identity & Data Security) Location: (India) Minimum Experience: 8+ years in Pre-Sales/ Solution Engineering or a similar senior technical customer-facing role

About the Role

The Sales Engineer is the technical authority and narrative driver in customer engagements. This role is responsible for translating complex AI, Identity, and Data Protection challenges into defensible, real-world solutions—while helping customers understand not just how something works, but why it matters. This role blends deep hands-on technical expertise with storytelling, big-picture ("paint the picture") thinking, and collaborative problem solving. You will work with prospects to obtain the technical win by designing a viable technical solution with an amazing product that can be applied to various scenarios. You will operate across AI security, LLMs, AI agents, MCP, DLP, and Identity, partnering closely with Sales, Product, and Engineering to close deals and influence product direction.

Responsibilities

Technical Discovery & Storytelling

Lead technical discovery for AI security, DLP, and Identity-driven use cases. Translate complex security architectures into clear narratives that resonate with: CISOs and VP's Security leaders Architects Engineers Business stakeholders "Paint the big picture" by connecting AI usage, identity trust, and data protection into a cohesive story. Clearly articulate what problems are solvable today, what are partially solvable, and what are structural limitations.

Architecture & Solution Design

Design and present end-to-end architectures covering: LLM and AI agent workflows (human + non-human identities) Data protection, inspection, and policy enforcement Identity, authentication, authorization, and trust boundaries Build, configure, and run demos and POCs using Dockerized components. Integrate enterprise environments including: Microsoft Entra ID (Azure AD), Okta Microsoft Intune, Jamf Pro Explain and demonstrate AI pipelines using: Lang Chain, Lang Graph MCP-style agent orchestration patterns Lead deep dives into DLP architectures, including: What DLP can realistically detect and enforce Where traditional DLP breaks down (AI, agents, encrypted channels, context loss) Common false assumptions customers have about DLP coverage Help customers understand enforcement gaps, bypass scenarios, and risk trade-offs without overselling.

Workshops & Collaborative Problem Solving

Facilitate technical workshops and brainstorming sessions with customer teams. Co-design architectures and controls with customers rather than prescribing static solutions. Whiteboard flows covering: AI access paths Identity trust boundaries Data movement and enforcement points Use workshops to uncover real constraints and drive solution alignment.

Execution & Field Innovation

Troubleshoot complex issues across AI pipelines, identity flows, and data paths. Create interim or custom solutions using Python, YAML, and cloud services when product features are missing. Collaborate with Product and Engineering to validate feasibility and relay field feedback. Maintain technical assets, demo configurations, and documentation in Git Hub.

Qualifications

Deep, practical understanding of Data Loss Prevention (DLP): Inspection, classification, enforcement points Real-world limitations and bypass scenarios Strong hands-on knowledge of: AI systems, LLMs, AI agents Emerging AI security risks and abuse patterns Solid foundation in Identity & Access Management (IAM): SSO, OAuth/OIDC, tokens, policy enforcement Practical experience with: Docker Linux Python YAML-based configuration Familiarity with endpoint and device trust tooling: Microsoft Intune Jamf Pro Experience with cloud platforms (AWS, Azure, or GCP). Comfortable using Git Hub for code, configs, and version control. Exceptional communication, storytelling, and executive-level presentation skills. Ability to lead workshops, whiteboard complex systems, and drive collaborative design sessions. Experience in an early startup environment preferred.

What Success Looks Like

Customers trust you not just as a technologist, but as a strategic advisor. Complex AI, identity, and DLP concepts are understood clearly at every stakeholder level. POCs reflect real customer constraints, not idealized lab conditions. Workshops result in alignment, not confusion. Deals move forward because solutions are credible, transparent, and defensible—even as the product evolves.