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Company Description
Quralyst is redefining how M&A sourcing gets done. Powered by AI, our platform equips investment banks, private equity firms, and corporate development teams with faster, smarter ways to identify and prioritize the right targets and buyers.
By transforming fragmented research into clear, actionable intelligence, Quralyst accelerates decision-making and unlocks new opportunities. We replace manual effort with intelligent automation—so dealmakers can focus on strategy, relationships, and closing transformative transactions.
We build practical AI systems that solve real problems. Small team, real ownership, fast growth.
We're not hiring for your resume. We're hiring for how you think. If you've spent weekends building something nobody asked you to, followed AI developments obsessively, or figured out a hard technical problem just because it bothered you — read on.
What you'll own
Backend and pipeline engineering
• Build, maintain, and extend multi-source data pipelines that fetch, merge, deduplicate, and enrich large datasets from external APIs
• Own long-running, cancellable background job infrastructure — including task state management, failure recovery, and real-time progress synchronisation over Redis and SSE
• Write production-grade async Python for heavy I/O workloads involving parallel external API calls, web scraping, and large in-memory dataset handling
• Integrate and manage high-volume LLM API usage (OpenAI, Anthropic) at production scale — including prompt engineering, rate limit handling, retry logic, and cost controls
Architecture and migration
• Develop new FastAPI services alongside the existing Flask monolith, with clean interface boundaries and shared infrastructure
• Progressively migrate Jinja-rendered views to a React TypeScript frontend without disrupting live production workflows
• Make architectural decisions on data modelling, caching strategy, and service decomposition that the rest of the team can build on
• Manage MongoDB schema evolution in a multi-tenant environment, including migration scripts and backward compatibility
Full-stack feature delivery
• Implement complete features across backend APIs, data layer, and frontend UI — not handing off between layers but owning the full vertical slice
• Build and maintain React components for data-heavy interfaces: enrichment results, CRM views, progress tracking, export workflows
• Maintain and extend Jinja templates in the existing application where React migration has not yet reached
Production operations
• Own Docker-based deployment, AWS infrastructure changes, and environment configuration
• Debug and tune production performance issues across the full stack — slow queries, memory pressure, API bottlenecks, and frontend rendering
• Write and maintain Pytest suites that give the team confidence to ship changes to complex, interdependent pipeline code
What We’re Looking For
Must-have
• 3+ years of proven experience shipping and maintaining production web applications — not prototypes or internal tools, but systems with real users and real consequences when they break
• Deep, practical Flask expertise: blueprints, application factory pattern, request lifecycle, auth, and API design — you have built non-trivial Flask applications, not just followed tutorials
• Strong FastAPI knowledge: you understand how it differs from Flask, when to use each, and how to run both in a shared codebase
• Production React experience with TypeScript, specifically on data-heavy interfaces — tables, real-time updates, async state, filtering on large datasets
• MongoDB in production: schema design for document stores, aggregation pipelines, indexing strategy, and multi-tenant data isolation
• Redis beyond caching: pub/sub, progress tracking patterns, and using Redis as a coordination layer for distributed background jobs
• Async Python mastery: you can write, debug, and reason about asyncio code under real I/O pressure, not just add ‘async’ to function signatures
• Production LLM integration experience: rate limiting, retry with backoff, prompt versioning, cost monitoring, and output validation — not just a few API calls in a demo
• Docker and AWS hands-on: you have deployed and operated containerised applications in cloud environments and can diagnose infrastructure-level issues
• Strong Pytest habits: you write tests for complex, stateful, I/O-heavy code, not just pure functions
Strong plus
• Experience maintaining Jinja templates in a Flask application and progressively migrating them to a React SPA
• SSE or WebSocket experience for real-time UI features in production
• OAuth implementation and multi-tenant SaaS patterns (org-level data isolation, per-tenant configuration)
• Exposure to M&A, investment banking, sales intelligence, or financial data — understanding the domain your users operate in makes you a meaningfully better engineer on this product
• Experience with billing and subscription management integrations (Stripe or similar)
• Familiarity with vector databases (Pinecone) and RAG pipeline architecture
You're a strong fit if you have
A visible history of curiosity — side projects, GitHub repos, hackathons, self-taught skills
Genuine interest in AI: you follow the field, you have opinions, you can articulate why it excites you
Hands-on Python you can actually use — write, debug, and figure things out from scratch
Some real exposure to LLMs, agents, automation, APIs, scraping, or backend basics (Flask, etc.)
We don't care about College brand · GPA · Fancy resume
We do care about Independent thinking · Proof of work · Genuine curiosity · Shipping real things
To apply: Send your resume + link to GitHub + link to proof of work (a project, a demo, something you built).
Click on Apply to know more.