Lead Developer

Brevan Howard; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Brevan Howard in Bengaluru seeks a senior software engineer to own backend development in Python, building scalable REST APIs and services. You will architect and optimize LLM-enabled pipelines, integrate LangGraph, MCP, and Airflow on AWS EKS, and collaborate with frontend teams.

The role requires 12+ years of Python experience, strong API design, and hands-on MongoDB/vector-store knowledge, with cloud expertise and a passion for quality engineering.

Qualifications

• 12+ years of professional software engineering experience with Python back-end focus.
• 1+ year hands-on with LLM-based applications (RAG, agent systems, prompt engineering).
• Deep Python expertise including async frameworks (FastAPI).
• Mongodb or similar document databases; vector stores desirable.
• Cloud and infra: AWS, Docker, Kubernetes, Airflow.
• API design: clean, versioned, production-ready RESTful APIs.
• Software practices: Git, pytest, CI/CD, Agile.
• Strong communication and collaboration skills.

Responsibilities

• Design, develop, and maintain Python back-end services and REST APIs using Python and FastAPI.
• Build and optimise LLM-based pipelines for extraction, summarisation, and classification.
• Develop agentic AI workflows using LangGraph, including tool orchestration and multi-step reasoning.
• Extend MCP server integration for third-party tools and workflows.
• Model data with MongoDB Atlas and vector stores for RAG pipelines.
• Create data processing and scheduling pipelines with Airflow on AWS EKS.
• Collaborate with front-end engineers on clean API contracts and data flows.
• Implement robust testing strategies and contribute to CI/CD and agile ceremonies.
• Monitor, troubleshoot, and improve system reliability and performance.

Skills

Python

FastAPI

Async

REST APIs

AWS

Docker

Kubernetes

MongoDB

Data Modeling

LLM AI

Tools

LangGraph

FastMCP

Airflow

AWS EKS

Job description

Key Responsibilities

• Design, develop, and maintain back-end services and REST APIs using Python and FastAPI, serving both internal and external consumers.
• Build and optimise LLM-based pipelines for information extraction, summarisation, and classification across diverse source types.
• Develop and maintain agentic AI workflows using LangGraph, including tool orchestration, multi-step reasoning chains, and feedback loops.
• Extend and operate the MCP server layer (FastMCP), enabling seamless integration of AI capabilities into third-party tools and workflows.
• Design and maintain data models and query patterns in MongoDB Atlas, leveraging both its document database and vector store capabilities for RAG pipelines.
• Build and manage data processing and scheduling pipelines using Apache Airflow, deployed on AWS EKS.
• Collaborate with front-end engineers to define clean, well-documented API contracts and ensure efficient data flows.
• Implement robust testing strategies (unit, integration, end-to-end) and contribute to CI/CD pipelines for reliable, automated deployments.
• Participate actively in agile ceremonies, code reviews, and architectural discussions, contributing to a culture of engineering excellence.
• Monitor, troubleshoot, and improve system reliability and performance across the platform.

Experience

• Years of Experience: 12+ years of professional software engineering experience, with a strong focus on Python back-end development.
• LLM & AI Development: At least 1 year of hands-on experience building LLM-based applications, including one or more of: retrieval-augmented generation (RAG), agent-based systems (e.g., LangChain, LangGraph), LLM-driven data extraction, or prompt engineering.
• Python Expertise: Deep proficiency in Python, including modern async frameworks (FastAPI or equivalent). Strong understanding of Python packaging, dependency management, and best practices.
• Database Skills: Experience with MongoDB or similar document databases. Familiarity with vector stores and embedding-based search is highly desirable.
• Cloud & Infrastructure: Practical experience with AWS services, containerisation (Docker, Kubernetes/EKS), and orchestration tools such as Apache Airflow.
• API Design: Proven ability to design, build, and document RESTful APIs that are clean, versioned, and production-ready.
• Software Engineering Practices: Strong grasp of version control (Git), testing frameworks (pytest, etc.), CI/CD pipelines, and agile development methodologies.
• Communication: Ability to articulate technical decisions clearly to both technical and non-technical stakeholders.

Nice to Have

• Experience with FastMCP or the Model Context Protocol (MCP) ecosystem.
• Familiarity with LangGraph or similar agent orchestration frameworks.
• Background in processing unstructured data from diverse sources (PDFs, audio, messaging platforms).
• Understanding of capital markets, investment research workflows, or financial data.
• Experience with observability and monitoring tools (e.g., DataDog, Prometheus, Grafana).
• Degree in Computer Science, Software Engineering, or a related discipline (or equivalent practical experience).