Role Description A well-funded seed-stage startup building the next generation of autonomous trading technology. You are building the intelligence layer on top of a purpose-built execution system for AI agents operating with real capital around the clock. What You'll Own - Learning System & RL Loop (~70%): - Design and implement the pipeline that connects live trade outcomes back to strategy improvement — signal quality, position sizing, timing, risk parameters. - Build the evaluation framework that separates genuine predictive signal from noise across agents, market conditions, and configurations. - Automate the strategy generation and testing cycle — the system should explore new configurations, validate them against real fleet data, and surface deployment candidates. - Detect regime shifts in market conditions and adapt fleet behavior accordingly. - Decompose every trade into its component drivers — signal quality, execution efficiency, exit timing — and wire those attributions back into strategy design. - Manage fleet-level coordination: concentration risk, capital allocation, and the exploration vs. exploitation balance. - Build the telemetry and data capture layer that makes all of the above possible. - Model & Inference Infrastructure (~30%): - Own the build-vs-buy decision on model hosting — evaluate proxied external APIs versus fine-tuned models on owned infrastructure and execute the chosen path. - Determine whether domain-specific training on trading data meaningfully outperforms prompted general-purpose models — then build the pipeline to act on that answer. - Optimize inference for the specific demands of a large autonomous agent fleet: concurrent agents, structured outputs, cost efficiency at scale. - Build the agent telemetry layer capturing every decision, signal score, and evaluation across the fleet. Qualifications - A production closed-loop system — model outputs drove real-world actions, outcomes were measured, and that feedback automatically improved the next decision. - Practical RL or online learning experience — you understand the challenges of learning from real-world feedback rather than static datasets. - Full-stack ML ownership — you build the pipeline, deploy the model, and own the outcome; Python primary, comfortable with Go or TypeScript in production services. - High-stakes sequential decision-making domain experience — finance preferred but not required; robotics, autonomous vehicles, game AI, ad bidding, and supply chain all transfer. Nice to Have - LLM fine-tuning and open-source model serving in production (vLLM, TGI, PEFT/LoRA). - Multi-agent system design. - Financial ML — signal generation, execution optimization, portfolio construction. - Onchain or DeFi experience. Interview Process - Fast — target first call to offer within two weeks. - Intro call with founders (60 min) — fit, motivation, your closed-loop experience. - Technical deep-dive (60 min) — open-ended system design, no right answer, evaluating how you think. - Paid trial project (1 week, part-time) if needed — real problem, compensated.

• Design, build, and deploy AI-powered applications on Azure • Collaborate with data engineers and business teams • Implement multi-LLM orchestration • Integrate AI solutions into business processes

• Design and implement AI/GenAI features across applications and SDLC workflows • Build AI solutions using platforms like Azure AI Foundry and LLM APIs • Develop agent-based workflows using frameworks such as LangChain, LangGraph, or Semantic Kernel • Implement RAG-based solutions and prompt engineering strategies • Leverage GitHub Copilot for AI-assisted development and productivity • Build full-stack applications using Python, JavaScript/TypeScript, and/or C# (.NET) • Develop APIs, backend services, and integrate with databases (SQL/NoSQL) • Ensure application quality through testing, monitoring, and observability • Collaborate with cross-functional teams (product, UX, data science) • Troubleshoot and optimize AI models, pipelines, and integrations

• Design and build the knowledge graph that represents structured content, its relationships, and its provenance. • Own the RAG retrieval pipeline end-to-end — chunking strategy, embeddings, hybrid retrieval, re-ranking, evaluation. • Drive measurable improvements in retrieval quality, hallucination rates, token economics, and latency. • Work directly with lighthouse customers to shape what "grounding" means in practice for enterprise AI deployments. • Contribute to the architectural foundations that the rest of Paligo's AI work will build on. • Partner with our CISO to keep what we build compliant with our security standards and data protection obligations.