AI Enterprise Architect - Agentic Systems

AI Enterprise ArchitectAbout us

Second Slope: bringing determinism to non-deterministic systems.

Enterprises are betting on AI that can't yet prove it works. We're a well-funded start-up building the fix from scratch, and you'll help shape it from day one. You'll work alongside industry-leading AI experts and serial founders, and take on niche, unsolved problems with academic experts from leading North American universities. If you'd rather build something meaningful than maintain something that already exists, this is the seat.

Role summary

Own the architecture and engineering of enterprise AI across its full lifecycle: designing and building AI and agentic systems, deploying them into production, scaling them across the enterprise, and operating them responsibly over time. AI is the organizing principle. Software and data engineering practices are applied in service of AI systems, so the code, pipelines, and platforms beneath them are built to make models and agents reliable, evaluable, and safe. You translate business strategy into platforms and solutions that are secure, cost-aware, and governed by design, and you are accountable for their technical integrity from first prototype through steady-state operations.

Architecture and design:

• Define target-state architecture, reference patterns, and standards for LLMs, agentic workflows, RAG, classical ML, and the data and integration layers beneath them

• Architect agentic systems end to end: orchestration, tool and API integration, memory and context management, human-in-the-loop controls, and multi-agent coordination

• Guide build-vs-buy-vs-partner decisions, model selection, and the prompting vs. retrieval vs. fine-tuning trade-offs

• Run architecture reviews and design authority forums across AI, application, and data workstreams

AI-led engineering (software, data, and AI):

• Set engineering standards for AI systems: evaluation-driven development, versioning of prompts, models, agents, and data, and test harnesses covering prompt regression, agent behavior, and safety

• Build production-grade AI applications: service and API design, modular and reusable components, clean separation of agent logic, business logic, and infrastructure, and secure coding

• Architect the data foundation for AI: batch and streaming pipelines, lakehouse and warehouse layers, feature stores, vector and knowledge stores, and pipelines for unstructured and multimodal data (chunking, embedding, indexing, refresh for RAG)

• Define data contracts, quality checks, lineage, and observability so that data feeding AI is trusted, governed, and monitored

• Own CI/CD and release engineering for models, agents, and applications: infrastructure as code, containerization, environment promotion, feature flags, release gating, and rollback