Forward Deployment Engineer

About the role

This role involves owning the technical integration of Smart Bricks' AI infrastructure into the workflows of sophisticated external organizations. It requires writing production code, building integrations, configuring AI agent workflows, and operating at the edge of what our system can do.

Responsibilities

• Design and build production-grade integrations between our API layer and external systems - data warehouses, portfolio management platforms, CRMs, and custom internal tools
• Deploy and configure AI agent workflows for specific operational contexts - translating general-purpose agent capability into precise, auditable, production-ready implementations
• Write production code, build data pipelines, and debug complex distributed systems in real-world client environments
• Collaborate closely with our core engineering team - translating what you observe in deployment back into product and infrastructure improvements
• Operate at the technical frontier of what our stack can do - finding the edges of the system, stress-testing assumptions, and expanding what is possible
• Engage directly with technical counterparts at sophisticated organizations - translating between their infrastructure and ours at depth, without losing technical fidelity in either direction

Requirements

We expect candidates to have a strong software engineering foundation - production code, API integrations, data pipelines, and cloud infrastructure. Experience deploying AI or ML systems in real-world environments is also required, along with comfort moving between deep technical implementation and high-stakes technical conversations without losing fidelity in either direction. Strong problem-solving instincts in ambiguous, fast-moving situations are necessary, as is taking ownership of outcomes, not just tasks. Candidates should be able to communicate technical complexity clearly to both engineering peers and non-technical stakeholders.

Qualifications

Nice to have includes experience in solutions engineering, technical implementation, or forward deployment at a high-growth AI or infrastructure company, familiarity with agentic AI systems, LLM tooling, or RAG pipelines, experience with GraphQL APIs, Apache Kafka, Snowflake, or Kubernetes, and background in financial services, enterprise software integration, or data-intensive production environments.

Benefits

Work on AI systems that are live in production - agentic orchestration, real-time inference, cross-market model transfer, and retrieval systems operating at scale on proprietary data that doesn't exist anywhere else. Build What's Next: The infrastructure we are building sits at the frontier of applied AI. The models, agents, and reasoning systems you work on here will define how one of the world's largest asset classes operates for decades. Ownership and Impact: Small team, no bureaucracy, high trust. Your work ships, your decisions matter, and your fingerprints are on everything we build. Learn from the Best: Collaborate with world-class engineers, researchers, and operators who left careers at leading AI labs and financial institutions to build something genuinely new.

Pay

Competitive salary commensurate with experience.

Schedule

Full-time, 40 hours per week.