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Onsite

Technical Lead

Recrew AI

Technical Lead

Skills

Google Cloud PlatformDockerReactDjangoPostgreSQLMachine LearningAIAWS

21 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

6 October 2026

What they need


Role: Tech Lead – Full-Stack AI Platform

Function: Engineering / Technology

Location: Bengaluru, Karnataka, India (Hybrid)

Type: Full-time

Industry: Accounting, AI/ML, SaaS, Fintech

About Company

An AI technology company building the accounting firm of the future. The company's platform acts as a specialized junior accountant within a human-in-the-loop model.

It targets independent accounting firms in North America, delivering productivity gains exceeding five times in key workflows. Operating in a $150B+ accounting market that still runs largely on manual effort, the company automates routine delivery and administrative tasks so accountants can focus on higher-value advisory work.

Backed by $6M in seed funding, the company is a lean, high-ownership team of eight, scaling fast from its Bengaluru base.

Position Overview

As Tech Lead, you will own a full engineering pod — setting technical direction, driving high-quality execution, and engaging directly with accounting firm customers to close the feedback loop. This is a 0-to-1 role at a seed-stage company: you will architect scalable AI agent and automation systems, hold the bar for your pod's engineering standards, and translate ambiguous customer problems into production-grade solutions. Your decisions will directly determine how fast the company can scale its platform across North America.

Role & Responsibilities

  • Own the full technical lifecycle of a pod — architecture, execution, code quality, and delivery — with direct accountability for timelines and outcomes
  • Engage directly with accounting firm customers to understand workflow pain points, validate solutions, and translate requirements into technical decisions
  • Architect and scale agentic AI systems and RAG pipelines built on LangChain/LangGraph, with full observability into agent behavior, latency, and failure modes in production
  • Drive 0-to-1 feature development: take ambiguous problem statements from first principles to production-ready, explainable code — no black-box vibe-coding
  • Design and operate pipelines that process large volumes of unstructured data — documents, receipts, financial records — with reliable parsing and structured output extraction
  • Set and enforce engineering standards within the pod — API design, test coverage for AI pipeline logic, model deployment hygiene, and incident ownership
  • Collaborate with founders and PMs on roadmap prioritization, proactively surfacing technical constraints and challenging requirements when the path doesn't make sense
  • Mentor and grow engineers within the pod, establishing a high-performance execution culture

Must Have Criteria

  • 5–7 years of software engineering experience, with at least 2 years in a tech lead or equivalent pod ownership role
  • Demonstrated 0-to-1 experience: has built and shipped a core product or system from scratch in a startup or early-stage environment
  • Has owned and resolved scalability challenges in production — throughput, reliability, or multi-tenant data volume at scale
  • 3+ years building and shipping production software as a full-stack engineer using Django/DRF, ReactJS, PostgreSQL, and REST APIs
  • Strong hands-on experience with agentic workflows, RAG pipelines, and model deployment in production — using LangChain, LangGraph, OpenAI/Anthropic APIs, and structured outputs
  • Has implemented observability for AI systems in production — tracing agent runs, monitoring model outputs, and debugging multi-step pipeline failures
  • Has worked extensively with unstructured data — documents, free-form text, or financial records — at production scale
  • Experience building B2B SaaS products with multi-tenant data models, RBAC, and audit trails
  • Track record of direct customer or stakeholder engagement — has translated customer requirements into technical decisions

Nice to Have

  • Has built or contributed to recommendation systems in a production environment
  • Familiarity with document OCR or financial document processing
  • Exposure to AWS or GCP, Docker, and workflow orchestration tools such as Temporal or Prefect
  • Prior domain exposure to tax, accounting, or financial document workflows
  • Experience at a seed or Series A startup with an engineering team of fewer than 15 people

What We Offer

  • End-to-end pod ownership from day one — technical, product, and customer dimensions
  • Direct collaboration with founders and PMs in a high-trust, low-bureaucracy environment
  • Ship to real users and see the impact of your work in weeks, not quarters
  • Hybrid work setup based out of Bengaluru, with exposure to North American customers and partners

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