Job Summary

We are looking for a GenAI Backend Developer / AI Engineer to design and develop scalable AI-powered applications and backend services. The role will involve building LLM-powered applications, RAG pipelines, AI agents, semantic search systems, multimodal AI solutions, and RESTful microservices using Python and FastAPI.

The candidate will work on enterprise AI use cases involving structured and unstructured data, document intelligence, conversational AI, intelligent search, and automated decision-support workflows.

Key Responsibilities

- Design and develop scalable backend services and RESTful APIs using Python and FastAPI.
- Develop and integrate Generative AI and LLM-based applications using models such as OpenAI and Gemini.
- Design and implement RAG pipelines for structured and unstructured data sources.
- Build semantic search and vector similarity solutions using vector databases such as FAISS, Qdrant, and BigQuery.
- Develop AI agents and tool-augmented workflows capable of retrieval, reasoning, and multi-step decision-making.
- Develop conversational AI and chat-with-database solutions for enterprise users.
- Work with multimodal AI models for image understanding, visual retrieval, and image-to-image semantic search.
- Build document intelligence solutions involving PDFs, PowerPoint files, images, and other unstructured data.
- Implement embedding-based retrieval and prompt engineering techniques to improve LLM response quality.




- Develop modular AI workspaces and reusable AI components for enterprise applications.
- Implement logging, monitoring, and observability mechanisms for AI/backend services.
- Collaborate with product, engineering, and business teams to translate business requirements into AI-powered solutions.

Required Technical Skills Programming & Backend

- Python
- FastAPI
- RESTful API development
- Microservices architecture
- PostgreSQL
- Asynchronous systems

Generative AI

- Generative AI / GenAI
- Large Language Models (LLMs)
- OpenAI / Gemini
- RAG
- Prompt Engineering
- Agentic AI
- Embedding models
- Semantic Search

Vector & Retrieval Technologies

- FAISS
- Qdrant
- BigQuery
- Vector similarity search
- Document retrieval and indexing

AI / Multimodal

- Multimodal AI
- Image understanding
- Image embeddings
- Visual/semantic search
- DINO / Grounding DINO

Cloud

- Google Cloud Platform (GCP)
- Microsoft Azure

Data & Workflow

- Structured and unstructured data processing
- PDF/document processing
- Workflow orchestration
- Temporal

Preferred Skills

- Experience developing enterprise GenAI platforms
- Experience with AI agents and tool calling
- Experience with multimodal AI applications
- Experience building production-grade RAG systems
- Experience with document intelligence and automated evaluation systems
- Experience integrating third-party APIs such as Google Calendar
- Experience with logging, monitoring, and production observability

📌 Gen AI Engineer (Mumbai)
🏢 BootLabs
📍 Mumbai