Tech Lead

• Build and own the core platform
• Design, build, and operate the systems that power an AI-driven platform end-to-end
• Data Platform: Architect pipelines ingesting real-time news and datasets from multiple vendors
• Scalable Infrastructure: Design and run Kubernetes systems handling events, documents, and concurrent AI workflows
• AI-Powered Intelligence: Build and integrate LLM-backed services for: Document insights and understanding (filings, reports, transcripts)
• Retrieval-augmented generation (RAG) using embeddings
• Intelligent research and analysis workflows
• Product APIs: Support web and mobile teams with reliable, well-designed backend APIs powering equity discovery, analytics, and AI features
• Keep production boring (Reliability is the feature)
• Platform stability under real market and user load
• Incident response, root-cause analysis, and postmortems
• Observability: metrics, logs, and traces that actually help debug AI and data issues

• Strong production experience with Node.js / Express
• Comfort building services in Python (FastAPI) for data and AI workloads
• Solid hands-on experience with: PostgreSQL — transactional correctness, schema design, and performance
• pgvector / vector databases — embeddings, similarity search, and retrieval trade-offs
• Redis — caching, pub/sub, real-time state
• Strong Kubernetes fundamentals (deployment, scaling, troubleshooting)
• Production experience integrating OpenAI or equivalent LLM APIs
• Hands-on experience with: RAG architectures
• Prompting, chunking, embedding strategies
• Managing cost, latency, and correctness trade-offs in LLM systems
• Production experience running systems on Cloud (Azure, GCP etc)
• Ownership of CI/CD pipelines (GitHub Actions, Cloud Build, etc.)
• Experience: 3+ years

• Built or operated AI-driven, data-intensive platforms
• Equity research, capital markets, or financial data exposure
• Experience debugging performance, latency, and reliability issues under load
• Clear opinions on: LLM and RAG architecture choices
• Data and embedding schemas
• Scaling and failure strategies
• Experience with workflow orchestration (e.g. Prefect, Airflow,) for: Data ingestion
• Model pipelines
• Long-running AI workflows
• History of taking systems from “works” → “works reliably at scale”

Competitive market salary + meaningful equity