AI Full Stack Engineer

Summary- We are seeking a highly motivated Finance AI Full Stack Engineer to design, develop, and deploy AI-powered finance solutions for enterprise customers.

Key Responsibilities

1. Design and develop end-to-end AI applications leveraging - Large Language Models (LLMs).
2. Retrieval Augmented Generation (RAG).
3. Multi-Agent Systems, Workflow Automation
4. Develop front-end applications and user interfaces.
5. Build backend services and APIs.
6. Design scalable microservice architectures.
7. Integrate AI services into enterprise applications.
8. Build secure and production-grade solutions.
9. Design enterprise knowledge bases.
10. Develop RAG pipelines.
11. Build document processing and semantic search capabilities.
12. Integrate structured and unstructured finance data.
13. Design ontology and knowledge graph models for finance domains.
14. Integrate AI applications with: o SAP ECC / S4HANA o Oracle ERP o Microsoft Dynamics o Data Platforms o Workflow Systems
15. Develop APIs and middleware services.
16. Enable real-time and batch integrations.

5. AI Performance Optimization

• Hallucination Control, Latency, Cost Efficiency, User Experience
• Implement evaluation frameworks and testing methodologies.
• Participate in customer workshops.
• Support solution demonstrations and PoCs.
• Translate business requirements into technical solutions.
• Drive end-to-end solution delivery.
• Provide technical guidance during implementation.

Qualification & Experience

1. 3â€6 years of hands-on full-stack software engineering experience, with proven delivery of production-grade LLM / Generative AI / Agent / RAG applications POC-only experience is insufficient.
2. Strong proficiency in Python and at least one modern frontend framework, such as React, Next.js, Vue or Angular.
3. Practical experience integrating LLM APIs and building prompt-engineering, RAG, tool-calling or agent-based workflows, with a solid understanding of model limitations, hallucination risks, context management and output reliability.
4. Hands-on experience developing backend services and APIs using frameworks such as FastAPI, Flask, Django, Node.js or equivalent, including authentication, authorization, error handling and asynchronous processing.
5. Practical experience with data ingestion, document processing, embeddings, vector search and databases such as PostgreSQL, Elasticsearch or a vector database.
6. Demonstrated experience delivering SaaS features across the full software-development lifecycle, including requirements clarification, technical design, implementation, testing, deployment, monitoring and production support