Python AI Engineer

Company Description Ultrabot Innovations specializes in Technology Consulting, helping businesses quickly access experienced engineers and technical specialists without the delays of traditional hiring. The company supports technology-driven organizations across India, the United States, Europe, and APAC in industries such as Fintech & Banking, Healthcare, Electronics, and Global Capability Centers. Ultrabot Innovations’ professionals work on enterprise platforms, cloud-native applications, data infrastructure, AI-powered solutions, and large-scale digital transformation programs. Core expertise includes Artificial Intelligence, Data Engineering, Cloud Technologies, and Product Engineering, with a strong focus on engineering quality, problem-solving, communication, and ownership. Clients rely on Ultrabot Innovations for talent that can thrive in fast-paced product environments, collaborate with distributed teams, and maintain high standards of code quality, scalability, security, and performance.
About the role -
We are looking for a Python Backend + AI Developer who is strong in backend engineering and has hands-on experience building and deploying production-grade GenAI applications.
The ideal candidate should have a solid foundation in Python backend development and either:
* At least 1 year of hands-on GenAI development experience, or
* At least one production-grade project involving RAG (Retrieval-Augmented Generation).
This is an engineering role—not a prompt-engineering-only position. You will be expected to build reliable backend systems and integrate AI capabilities into production applications.
Key Responsibilities
* Design, develop, and maintain scalable Python backend services and APIs.
* Build and integrate LLM-powered applications and AI features.
* Develop production-grade RAG pipelines, including document ingestion, chunking, embeddings, retrieval, reranking and response generation.
* Work with LLM providers such as OpenAI, Anthropic, Gemini or similar models.
* Build APIs using frameworks such as FastAPI, Flask or Django.
* Work with relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, Redis, etc.
* Work with vector databases / vector search technologies such as FAISS, Pinecone, Qdrant, Weaviate, pgvector or equivalent.
* Implement authentication, authorization, logging, error handling and API performance optimization.
* Integrate AI services with existing backend systems and third-party APIs.
* Write clean, maintainable and well-tested production code.
* Collaborate with frontend, AI/ML and engineering teams to deliver end-to-end features.
* Troubleshoot production issues and improve system reliability, scalability and performance.

Work arrangement
Hybrid