Descripción de la oferta
Optiak is the modular operating system for enterprise AI. We extract the platform logic companies normally scatter across applications integrations, routing, security, memory, observability, prompt enrichment, and optimization into seven standardized layers that work with any model and any third‑party tool, under one governed framework.
A platform like ours lives or dies on how well it connects to the customer’s world: their ERPs, CRMs, data lakes, document stores, internal APIs, and legacy systems. We’re hiring a founding architect to own that problem end to end.
This is a deliberately unusual role. Most companies split it into three. You’ll do all three, and you should want to:
Build the platform primitives that make Optiak integration‑ready out of the box.
Architect integration strategies with customers as a hands‑on, technical solutions partner in pre‑sales.
Deploy those integrations inside real, regulated enterprise environments as a forward deployed engineer.
WHAT YOU'LL WORK ONBuild the integrations platformYou’ll own Optiak’s Integrations Layer and Prompt Enrichment Layer, the parts of the platform responsible for moving the right data to the right place, securely.
Design and ship pre‑built connectors to major enterprise systems Salesforce, SAP, HubSpot, BigQuery, Google Drive, Slack, and the long tail of internal APIs so customers integrate in days, not quarters.
Build the connection module to internal systems: ingestion, normalization, indexing, and permission‑checked retrieval across both structured and unstructured data.
Own the prompt‑enrichment data flow that decides what data a query needs, where it lives, and the most efficient way to retrieve it before a prompt reaches a model.
Design integration primitives for MCP and other emerging interoperability standards, so Optiak is the easiest platform to plug into any business ecosystem.
Make the core architectural calls: when to copy data, when to federate, and when to query in place.
Architect with customers (pre‑sales)Work alongside the founders in pre‑sales as the technical authority on integration: scope the customer’s data estate, surface constraints early, and design the right integration approach for each deployment.
Translate messy enterprise reality legacy ETL, SSO/SAML, data residency, governance into a concrete architecture the customer’s own engineers trust.
Be hands‑on, not slideware. Prototype, demo against real systems, and earn credibility with customer technical teams.
Deploy in the field (forward deployed)Embed with design partners and early enterprise customers to take integrations from architecture to production inside their environment.
Nawigate security reviews, compliance constraints, and the politics of getting production credentials the work that actually decides whether an AI deployment ships.
Codify what you learn into reusable connectors and patterns, and feed it straight back into the platform roadmap.
WHAT WE ARE LOOKING FORCore8+ years in backend, platform, or systems engineering, with deep experience in enterprise integrations and complex system architecture.
Genuinely hands‑on. You write production Python today and want to keep doing so; this is not a role that drifts into pure advisory work.
Strong data‑systems background: data lakes, APIs, batch and streaming pipelines, storage, and indexing.
Proven track record designing systems used by large organizations at scale, multi‑tenant, secure by default, observable, and auditable.
JudgmentYou think in systems, not services. You’ve designed platforms meant to be extended, not shipped once.
You make architectural tradeoffs across performance, security, and operational complexity under real constraints.
Customer‑facingYou’re comfortable in front of customers scoping, designing, and defending an architecture with their technical leaders and you’ve worked inside regulated environments (security, compliance, data residency).
You can switch between writing code and explaining a design to a CISO in the same day.
AI‑aware (bonus, but it matters here)Familiarity with retrieval architectures, RAG, context assembly, and how data and integration choices shape LLM latency, cost, and quality.
Experience supporting AI products in production.#J-18808-Ljbffr
