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

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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.

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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

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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.)

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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”

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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”

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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.