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Tradeify

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

💳 Fintech

💸 Finance

Fintech • Finance

Tradeify is a proprietary trading firm and fintech platform that funds retail futures and crypto traders through evaluation challenges, simulated (Lightning Funded) accounts, and live funded accounts. It provides integrated trading platform access, built-in trade journaling and analytics, fast automated payouts, clear risk rules, and community/educational content to help traders scale capital and transition to live trading.

📋 Description

• Multi-step AI agents that handle complex, real-world workflows end to end • RAG pipelines that actually perform — not just LangChain defaults • Durable background workflows for processing, enrichment, and orchestration • Internal dashboards, admin tools, and integration surfaces (Slack, Discord, Intercom) • Eval harnesses to measure and improve model performance over time • The glue layer between LLMs, APIs, databases, and product features

🎯 Requirements

• Python 3.12 as your primary language — you live in the AI/ML ecosystem; FastAPI for AI/agent service APIs • TypeScript (strict) in a monorepo on Bun + Turborepo — Next.js (App Router), React, Tailwind, shadcn/ui for UI; Hono + tRPC for the API layer; Better Auth for authentication • Comfortable with SQL and Drizzle ORM with PostgreSQL — comfortable querying and working with a relational data model • Hands-on with Anthropic, OpenAI, and Google Vertex AI (Gemini) APIs — function/tool calling, structured outputs, prompt caching; multi-provider failover via OpenRouter and Cloudflare AI Gateway • Production experience with an agent framework: LangGraph for agent/AI services — not just a weekend project • Familiar with Model Context Protocol (MCP) — building and consuming MCP clients/servers to extend agent capabilities • You know the boring-but-critical stuff: streaming, retries, token cost management, rate limiting • Experience with vector DBs: pgvector (preferred), Pinecone, Weaviate, or Qdrant • Intentional about embedding models and chunking strategy — you've reasoned about trade-offs, not just accepted defaults • Implemented hybrid search (BM25 + vector) when precision matters • Code-first workflow experience: BullMQ + Redis for queues, scheduled jobs, and durable background orchestration • Aware of low-code tools: n8n, Zapier, Make — can use them when appropriate • Background jobs, queues, and scheduled tasks are second nature • Active user of at least one eval/observability platform: Langfuse (self-hosted) for tracing and evals — our primary observability platform; familiarity with LangSmith, Braintrust, or Helicone is a plus • You've actually run an eval set — you have opinions on what makes a good one • Claude Code is your daily driver — plus comfort operating within a homegrown agentic-dev layer (hooks, skills, subagents) • You ship internal tools in days, not sprints • You have a 'vibe coding' instinct — but you don't let it erode eng discipline • Comfortable deploying on AWS (EC2, S3, SES, ECR, Parameter Store) in us-east-2; Docker / Docker Compose for dev and prod; Caddy for TLS and reverse proxy; GitHub Actions + OIDC for CI/CD • Docker / Docker Compose, env management, and secrets handling (AWS Parameter Store) are table stakes • Some Biome, Vitest, and Playwright familiarity is a plus for linting, unit testing, and end-to-end testing • Can spin up Next.js (App Router) / React dashboards and admin interfaces when needed • Familiar with Discord.js, Slack API, and Intercom API; comfortable with Asana, Fellow, and Google Workspace (Drive/Calendar/Gmail) integrations • Understands product instrumentation with Mixpanel or GA4

🏖️ Benefits

• You'll work on a real, fast-moving product. • AI is a first-class priority, not an afterthought. • Small team, high ownership. • The domain is genuinely interesting. • We use the tools we ask you to know.