Lead Developer
Brevan Howard Investment Management5 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Design, develop, and maintain Python backend services and production REST APIs using FastAPI.
- Build and optimize LLM-based extraction, enrichment, summarization, and classification pipelines.
- Develop and maintain LangGraph agentic workflows with tool orchestration, multi-step reasoning, and feedback loops.
- Extend and operate the FastMCP/MCP server layer for integrating AI capabilities with third-party tools and workflows.
- Design MongoDB Atlas data models and query patterns, including document database and vector store capabilities for RAG pipelines.
- Build and manage Apache Airflow data processing and scheduling pipelines deployed on AWS EKS.
- Collaborate with frontend engineers and product stakeholders on API contracts and efficient data flows.
- Implement unit, integration, and end-to-end testing and contribute to CI/CD pipelines.
- Monitor, troubleshoot, and improve platform reliability and performance.
- Participate in code reviews, architectural discussions, and agile ceremonies.
Requirements
- 10–17 years of professional software engineering experience focused on Python backend development.
- At least one year of hands-on experience building LLM-based applications, such as RAG, agent-based systems, LLM-driven extraction, or prompt engineering.
- Deep Python proficiency, including modern asynchronous frameworks such as FastAPI, Python packaging, dependency management, and best practices.
- Experience with MongoDB or similar document databases; familiarity with vector stores and embedding-based search is preferred.
- Practical experience with AWS, Docker, Kubernetes/EKS, and Apache Airflow.
- Proven ability to design, build, and document clean, versioned, production-ready RESTful APIs.
- Strong understanding of Git, testing frameworks such as pytest, CI/CD pipelines, and software engineering practices.
- Ability to communicate technical decisions clearly to technical and non-technical stakeholders.
- Experience with FastMCP or the MCP ecosystem and familiarity with LangGraph or similar agent orchestration frameworks are preferred.
- Experience processing unstructured data from PDFs, audio, or messaging platforms is preferred.
- Understanding of capital markets, investment research workflows, or financial data is preferred.
- Experience with observability and monitoring tools such as DataDog, Prometheus, or Grafana is preferred.
- Degree in Computer Science, Software Engineering, or a related discipline, or equivalent practical experience.
Tech Stack
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