Senior Python Engineer - AI Agents, Forecasting

Responsibilities

• Unifying the different parts of the stack. The network, signal layers, and forecasting architectures need to work together as one coherent system.


• Designing and building the agent orchestration pipeline that allows AI forecasters to ingest data, reason, and produce predictions.


• Building and optimising the signal pipeline that feeds real-world data into forecasting models.


• Experimenting with different forecasting architectures to find optimal approaches for linking targets to signals.


• Writing production-grade Python that handles complexity at scale, not scripts that work in a notebook.


• Contributing to technical strategy alongside the founders. You'll have a voice in what gets built and why.


• Evaluating and vetting technical candidates as the engineering team grows.

Requirements

• 5+ years building production-grade Python backends. You know the internals, not just the syntax.


• Hands-on experience with LLM orchestration frameworks such as LangChain or LangGraph (agent memory, tool calling, state management)


• Dagster in production (assets, sensors, partitions) or equivalent pipeline orchestration


• Strong backend fundamentals including API design, async programming, and database modelling


• You've built systems that had to work reliably at scale, not just pass a demo


• Worked at an early-stage startup or high-growth environment. You understand the pace.


• Built and shipped production systems, not just prototypes


• Comfortable being the most senior engineer in the room, or the only one


• You think holistically about systems. You see how your work connects to every other part of the product without being told.


• Research-driven approach to problem solving. You test hypotheses, not just ship features.


• Experience in financial markets, algo-trading, or prediction market platforms (Polymarket, Manifold, etc.)


• Quantitative background in maths, statistics, or probability theory


• ML experience including random forests, regression models, scikit-learn, and PyTorch


• AWS infrastructure experience deploying containerised applications and managing cloud environments (EC2, Lambda, RDS)


• Open-source contributions to AI or crypto projects.

Core Competencies

Demonstrates expertise in building production-grade Python backends and orchestrating complex data pipelines, with a strong focus on system reliability and scalability. Possesses a quantitative background and experience in financial markets, enabling effective problem-solving and strategic contributions to technical direction.

Highest-signal resume keywords

• Production-Grade Python Development


• LLM Orchestration Frameworks


• Dagster Pipeline Orchestration


• Backend Fundamentals


• AWS Infrastructure Experience

ATS Optimization Keywords
Hard Skills

• Python


• API Design


• Async Programming


• Database Modelling


• Random Forests


• Regression Models


• Scikit-Learn


• PyTorch


• Mathematics


• Statistics

Soft Skills

• Holistic Thinking


• Research-Driven Problem Solving


• Leadership

Industry Keywords

• Financial Markets


• Algo-Trading


• Prediction Market Platforms


• Open-Source Contributions

Tools & Technologies

• LangChain


• LangGraph


• Dagster


• AWS EC2


• AWS Lambda


• AWS RDS