Tech Lead

• Experience: 3+ years
• Location: Bangalore (On-site, HSR Layout)
• Works with: Backend, Data, AI/LLM, Product
• Reports to: Founder
• Compensation: Competitive market salary + meaningful equity

This Role Is Ideal For Someone Who…

• Enjoys building serious platforms, not CRUD dashboards
• Thinks in data flow, latency, failure modes, and correctness
• Has strong architectural opinions and can justify them from first principles
• Likes being close to production systems, incidents, and real users
• Wants to shape the technical DNA of an Finance AI platform from early stages

What You’ll Actually Be Responsible For

1. Build and own the core platform

You will 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

This role is about owning the platform, not contributing isolated services.

2. Keep production boring (Reliability is the feature)

You are directly responsible for:

• 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

If something breaks, you are expected to:

• Understand what failed (data, infra, model, or orchestration)
• Fix it decisively
• Put safeguards in place so it doesn’t recur

Core Technical Expectations (Non-Negotiable)

Backend & Systems

• Strong production experience with Node.js / Express
• Comfort building services in Python (FastAPI) for data and AI workloads

Data, AI & Infrastructure

• 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)

AI / LLM Integration

• 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

Cloud & DevOps

• Production experience running systems on Cloud (Azure, GCP etc)
• Ownership of CI/CD pipelines (GitHub Actions, Cloud Build, etc.)

Signals That Strongly Help

• 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”

This Role Is Not For Someone Who…

• Wants to stay abstract and avoid production ownership
• Prefers predictable roadmaps and neatly scoped problems
• Thinks DevOps or data reliability is “someone else’s responsibility”

Why This Role Is Interesting (If You’re the Right Person)

• You’ll help define the technical backbone of a serious finance product
• You’ll work on problems where data correctness, AI reliability, and latency matter daily
• You’ll make architectural decisions with long-term impact
• You’ll operate close to markets, data, and real user decisions

This role rewards:

• Judgment over buzzwords
• Depth over breadth
• Ownership over titles

If you enjoy building high-signal, high-reliability AI platforms in early-stage, high-accountability environments, this role is intentionally designed for that.