AI Agent Engineer
unreconciled
Date: 20 hours ago
City: Ahmedabad, Gujarat
Contract type: Full time

About Nourma
We're building the AI-powered finance operating system that transforms how companies manage their financial operations. Our Decision Intelligence platform combines LLMs, multi-agent systems, and real-time data integration to create an intelligent finance team
We're seeking an AI/ML Engineer with deep expertise in LangChain, LlamaIndex, PydanticAI, and modern Python frameworks to architect and build the core intelligence layer of Nourma.
- Architect complex LangChain pipelines for multi-agent financial workflows
- Build production RAG systems using LlamaIndex for financial document retrieval
- Implement agents with strong type safety and structured outputs
- Design and implement:
- Chain-of-thought reasoning for financial analysis
- Dynamic prompt routing based on query complexity
- Memory management for long-running financial conversations
- Tool integration for agents to access GL, bank feeds, and operational data
- Optimise token usage and response latency for real-time WhatsApp interactions
- Build high-performance FastAPI services for:
- Agent-to-agent communication protocols
- WhatsApp webhook processing with sub-second response
- Real-time financial data APIs for frontend consumption
- Design GraphQL schemas for flexible financial data queries
- Implement WebSocket connections for live financial updates
- Create robust error handling and retry mechanisms for financial integrations
- Design and optimise Chroma collections for:
- Financial document embeddings (loan agreements, invoices)
- Conversation history and context retrieval
- Business logic and rule storage
- Implement hybrid search combining vector similarity and metadata filtering
- Build embedding pipelines for various document types (PDFs, emails, chat logs)
- Deploy and manage LLM applications.
- Implement Redis caching strategies for LLM responses and financial data
- Design microservices architecture for agent deployment
- Set up monitoring and observability for AI pipelines
- Expert-level proficiency in:
- LangChain: Custom chains, agents, tools, memory systems
- LlamaIndex: Document stores, indices, query engines
- PydanticAI: Agent frameworks, type-safe LLM interactions, structured outputs
- FastAPI: Async programming, dependency injection, middleware
- Strong experience with Python async/await patterns
- Production experience with Chroma or similar vector databases
- Proficiency with Redis for caching and session management
- Experience with data pipeline and storage tools (Kafka, Spark, Airflow) for building scalable systems
- Knowledge of PostgreSQL and BigQuery for analytical workloadsUnderstanding of financial data structures (journal entries, chart of accounts)
- Experience with financial APIs (QuickBooks, Xero, Plaid, banking APIs)
- Knowledge of data consistency requirements for financial systems
- GraphQL schema design and optimisation
- Experience with WhatsApp Business API
- Background in fintech or accounting software
- LLMs: GPT-4, Claude, open-source models
- ML/AI: LangChain, LlamaIndex, PydanticAI, PyTorch, Transformers
- Vector DB: Chroma
- Data: PostgreSQL, BigQuery, Apache Kafka, Spark, Airflow
- APIs: FastAPI, GraphQL
- Infrastructure: AWS/GCP, Kubernetes, Docker, Redis
- Monitoring: Prometheus, Grafana, OpenTelemetry
- Work on cutting-edge problems combining LLMs with real-time financial data
- Build systems processing millions of financial transactions
- Direct impact on how thousands of companies manage finances
- Work directly with founders and shape technical direction
Ideal Candidate Profile
You're excited about:
- Building production LangChain and PydanticAI applications at scale
- Creating high-performance APIs that power AI agents
- Designing scalable architectures for financial data processing
- Working with cutting-edge LLM technologies
You've probably:
- Built production LangChain/LlamaIndex/PydanticAI applications serving 1000+ users
- Created FastAPI services handling high-throughput LLM requests
- Worked with vector databases in production environments
- Designed data processing pipelines for financial or similar domains
- Contributed to open-source AI/ML projects
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