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Overview:

Synapt Context Substrate is the trusted context layer that sits between enterprise data and AI agents — organizing knowledge, procedures, relationships and decisions so that every agent reasons from the same governed, current source of truth. We are looking for a Technical Lead to join the Customer Success (FDE) team and own the technical arc of customer engagements end to end: from spotting a genuine substrate problem inside a customer's environment, through building the demo that proves it, to running the pilot and carrying it into production.

This is a hands-on, customer-facing technical leadership role. You will not hand off between “the person who sells it” and “the person who builds it” — you are both, across every account you own. You will work directly inside the team's three-stage engagement model — Introduce, Prove, Establish — and be the technical backbone that moves an account from first conversation to a production deployment the customer treats as standard infrastructure, not a project.

Responsibilities:

• Use-Case Design (Introduce stage)

• Read a customer's estate — systems, workflows, existing tools — rather than waiting for a fully-formed requirement, and diagnose whether the problem is genuinely a substrate problem: a reasoning or judgment gap over fragmented knowledge, not an integration, ML, or reporting problem in disguise.
• Partner with account and sales teams to shape one well-scoped use case per engagement, built from the customer's own documents and systems, that can produce a measurable result in 3–6 weeks.
• Exit this stage with the customer naming one use case and the KPI it will be judged on — not general enthusiasm.
• Customer Demonstrations

• Design and deliver technical demos and proofs-of-concept that ground the pitch in the customer's own data and systems rather than generic examples.
• Translate the substrate's architecture and value proposition (a shared context foundation across every agent, always-current knowledge, customer-hosted data sovereignty, hallucination-free grounded answers) into a story specific to that account and its stakeholders.
• Present confidently to technical and executive audiences on the customer side, including in high-stakes leadership demos.
• Pilot Delivery (Prove stage)

• Lead pilots inside the customer's own environment, riding an engagement or cadence that already exists rather than triggering a new procurement cycle.
• Instrument the pilot against a KPI the customer already reports, and drive it to a number the customer will repeat to their own leadership without your team in the room.
• Manage pilot scope, timeline and technical risk directly — this role builds and debugs, it does not only direct others.
• Production Deployment (Establish stage)

• Own the technical path from a proven pilot to a production deployment: architecture decisions, integrating the customer's live systems as tools that stay outside the substrate rather than being rebuilt inside it, and governance and data-sovereignty requirements.
• Identify and scope the next domains that can run on the same substrate, without a rebuild, as the natural expansion path.
• Exit this stage when the substrate is the customer's default context layer — a standard, not a project.
• Cross-Account Technical Leadership

• Mentor and support other Field-Deployed Engineers; bring technical rigor and consistency across the account portfolio.
• Turn account-specific patterns into reusable use-case archetypes the rest of the team can apply elsewhere.
• Apply the team's stage-gate discipline — evidence, not enthusiasm: no move to Prove without a named KPI, no move to Establish without a measured result.

Requirements:

• 8–12 years of experience in technical/solutions engineering, forward-deployed engineering, technical pre-sales, or applied AI/ML delivery, with substantial direct customer-facing experience.
• Proven delivery of AI/LLM-based systems (agents, RAG, knowledge graphs, or comparable context/knowledge infrastructure) inside real enterprise environments — not only prototypes or hackathon-style builds.
• Full-arc ownership, comfortable moving between discovery conversations, demo-building, pilot execution and production rollout on the same account — this role spans both pre-sales and delivery, not one or the other.
• Strong systems thinking, able to tell a genuine reasoning/context problem apart from an integration, ML, or reporting problem — and to say no to the latter.
• Hands-on technical depth, able to build and debug proofs-of-concept directly; working knowledge of enterprise data integration, APIs, and typical AI/cloud stacks (e.g. Python, common cloud platforms).
• Business fluency, able to translate technical architecture into KPI and business-outcome language for non-technical and executive stakeholders.
• Structured pilot experience, defining baselines, instrumenting measurement, and presenting results that customers repeat to their own leadership.
• Excellent communication and executive presence in live customer settings, including with senior client stakeholders.