Senior Backend Engineer (Fintech)

Zoho; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Zoho is hiring a Senior Backend Engineer (Python / AWS) to build a production automated order placement system for an AI-driven trading client. You will design an explicit order state machine, implement resilient brokers, and ensure full auditability of signals, orders and fills.

Responsibilities include reliability, reconciliation, and governance, plus ownership and collaboration across teams. You will deploy on AWS with Terraform, CI/CD, and observability tooling, driving production readiness

Qualifications

• 5–7+ years of backend development, owning end-to-end production systems.
• Degree in CS or related field; equivalent experience considered.
• Strong Python skills with asyncio, typing, testing and FastAPI.
• Hands-on Redis experience: structures, pub/sub, streams, locks, persistence trade-offs.
• Strong AWS experience (ECS/EKS, Lambda, SQS/SNS, RDS, IAM, Secrets Manager, CloudWatch) and Terraform/ IaC.

Responsibilities

• Design and build the order management layer with a state machine (new, sent, filled, etc.).
• Ensure idempotent order submission and pluggable broker adapters via REST/WebSocket/FIX APIs.
• Implement pre-trade risk checks and per-order governance features.
• Build intraday and end-of-day reconciliation against broker records with audit trails.
• Develop event-driven services in Python and integrate with Redis and AWS services.
• Add metrics, tracing and dashboards for latency and errors.
• Collaborate on architecture, write runbooks, and mentor engineers.

Skills

Python

Asyncio

Typing

Testing

FastAPI

Distributed systems

Education

B.E./B.Tech/B.S. in Computer Science

Tools

Redis

Docker

Terraform

CloudFormation

PostgreSQL

Prometheus

Grafana

Datadog

CloudWatch

Kafka

Kinesis

Job description

We are hiring a Senior Backend Engineer (Python / AWS) to build a production automated order placement system for an AI-driven systematic trading client.

The system turns trading signals into broker orders, tracks every order through its lifecycle and feeds fill results back to the client's portfolio optimizer. It must run reliably and safely, with a full audit trail.

Responsibilities

1. Order management and trading logic

• Design and build the order management layer: an explicit order state machine (new, sent, partially filled, filled, cancelled, rejected, expired).
  
• Make order submission idempotent, so retries and reconnects never create duplicate orders.
  
• Build a pluggable broker adapter for submit, cancel, replace, status and fill events over REST, WebSocket or FIX APIs.
  
• Implement pre-trade risk checks: position and notional limits, per-order size limits, restricted lists, price sanity checks and a manual plus automatic kill switch.
  
• Aggregate fills into positions and residual quantities, and pass a clean state snapshot to the optimizer each cycle.
  
• Implement configurable policies for unfilled orders: cancel, re-price or carry over.
  

2. Reliability, reconciliation and audit

• Build intraday and end-of-day reconciliation of orders, fills and positions against broker records, with break reports and alerts.
  
• Maintain an immutable, event-sourced audit trail of every signal, order, broker message and fill.
  
• Handle broker rate limits, outages and undocumented behaviour with retries, backoff and stale-order detection.
  
• Test partial fills, rejects and disconnects end to end in paper trading before any live order is placed.
  
• Build asynchronous Python services integrated with the client's existing codebase.
  
• Use Redis for fast order and position state, caching, locks and messaging.
  
• Deploy on AWS (ECS, Lambda or EKS) with the platform engineer, using Terraform, CI/CD and separate dev, paper and prod environments.
  
• Add metrics, tracing and structured logs, with dashboards and alerts for latency, errors and stuck orders.
  

4. Ownership and collaboration

• Take part in discovery and the architecture proposal, and raise concerns about design or requirements early.
  
• Write runbooks and architecture docs.
  
• Review code and share best practices with the team.
  

Requirements

Requirements

• 5–7+ years of experience in backend development, with production systems you have owned end to end.
  
• B.E. / B.Tech / B.S. in Computer Science or a related field. Candidates without these degrees but with strong experience in the areas below will be considered.
  
• Strong Python skills, including asyncio, typing, testing and a modern framework such as FastAPI.
  
• Hands-on production experience with Redis: data structures, pub/sub or streams, distributed locks and persistence trade-offs.
  
• Strong AWS experience (ECS/EKS, Lambda, SQS/SNS, RDS, IAM, Secrets Manager, CloudWatch) and Infrastructure as Code (Terraform, CloudFormation or equivalent).
  
• Solid work with PostgreSQL or another relational database, including transactions, consistency and schema design for financial records.
  
• Proven design of distributed, event-driven systems: idempotency, exactly-once or at-least-once processing, retries, failure handling and state machines.
  
• Finance knowledge is a must: positions, P&L, notional exposure, portfolio rebalancing, and how trades are booked and settled.
  
• Trading knowledge is a must: order lifecycle, order types (market, limit, stop), partial fills, slippage, pre-trade risk controls and broker reconciliation.
  
• Hands-on experience integrating with at least one broker or exchange API.
  
• Experience with Docker, CI/CD pipelines and observability tools (Prometheus/Grafana, Datadog or CloudWatch).
  
• An ownership mindset: you drive work to production, raise risks early and write clear documentation.
  
• Experience mentoring engineers and running code reviews.
  
• Clear written and spoken English, for working directly with the client's engineering leadership.
  

Nice to have

• Prior work at a hedge fund, prop trading firm, brokerage, fintech or quant platform.
  
• Built an order management system (OMS) or execution management system (EMS) that went live.
  
• Exposure to systematic or quantitative trading: signals, portfolio optimizers, backtesting and paper trading.
  
• Execution-quality work: order slicing (TWAP/VWAP), slippage and transaction-cost analytics.
  
• Apache Kafka or Kinesis for event streaming.
  
• Awareness of audit, compliance and data-retention needs in regulated trading.