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Open nowPosted 70 days ago

Lead Developer

brevanhoward24 open roles

Where
Bangalore
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Your applicationOpen nowLead Developerbrevanhoward · Bangalore

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The clock on this job

Early applications get read.

13.9% of postings close within 7 days. Measured by our own scanner across the market. brevanhoward postings stay open a median of 34 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.9%7 days
  4. 22.4%14 days
  5. 38.1%30 days
  6. 59.0%60 days
This job: posted 70 days ago

brevanhoward median: 34 days open

The posting

About the Role

As a Lead Developer, you will own and evolve the core platform services that power our AI-driven products. You will design and build production-grade APIs, LLM-based extraction and enrichment pipelines, and agentic workflows — ensuring they are reliable, performant, and scalable.

You will work closely with AI engineers, front-end developers, and product stakeholders to translate investment use cases into robust back-end solutions.

This role is ideal for an engineer who combines deep Python expertise with hands-on experience building LLM-driven applications and who thrives in a fast-paced, product-oriented environment.

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: 10–17 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).
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