AI Engineer

About Smart Bricks

Smart Bricks is a frontier AI lab building autonomous reasoning systems that allow capital to discover, evaluate, and transact assets end-to-end. We are a team working on genuinely hard problems at the intersection of AI infrastructure and one of the world's largest industries. Everything we build is live in production, consequential, and compounding in capability with every data point the system generates.

As an AI Engineer at Smart Bricks, you will work across our full model and agent stack in production. This is not a demo environment—everything we build is live, consequential, and improving with every data point the system generates. You will work on agentic orchestration, production model deployment, real-time data infrastructure, and the feedback loops that make the system smarter over time.

You will own what you build end-to-end, from research and experimentation through to deployment and live monitoring.

• Building, deploying, and maintaining production AI models across valuation, forecasting, scoring, and classification tasks—validated against real-world outcomes
• Developing and optimising Lang Chain-based agentic orchestration workflows and contributing to our custom agent framework
• Designing and improving our RAG pipeline and vector search infrastructure for domain‑specific retrieval and explainability
• Collaborating with data engineers to improve feature store quality, coverage, and serving latency
• Building and maintaining model monitoring infrastructure—drift detection, performance tracking, and automated retraining triggers
• Contributing to the full MLOps lifecycle—experiment tracking, model versioning, deployment pipelines, and rollback protocols

• Have strong production ML engineering experience—model training, evaluation, deployment, and live monitoring
• Be comfortable building and maintaining agentic systems using Lang Chain or equivalent orchestration frameworks
• Have hands‑on experience with LLMs, RAG pipelines, and vector search infrastructure
• Be proficient in Python and comfortable working across PyTorch or Tensor Flow
• Have experience deploying containerised ML systems—Docker, Kubernetes, AWS
• Take full ownership of what you build and care deeply about model quality in production
• Communicate clearly across engineering and research—you work at the boundary between both

• Experience with gradient‑boosted models (Light
  
  GBM, XGBoost) and geospatial ML
• Familiarity with Snowflake, Pinecone, Apache Kafka, or Spark
• Background in applied AI in a financial services or data‑intensive production setting
• Experience with serverless inference—AWS Lambda or equivalent

• Solve Hard Problems:
  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.