Join our team as a Custom Software Engineer and contribute to innovative software development. You will design, code, and enhance components across various systems and applications. Our focus is on leveraging modern frameworks and agile methodologies to deliver scalable, high-performing solutions perfectly aligned with unique business requirements.

Develop and maintain sophisticated AI-powered applications using Python. Build robust enterprise GenAI solutions, integrating Large Language Models (LLMs) and Natural Language Interfaces. Create and optimize Retrieval-Augmented Generation (RAG) pipelines, Text-to-SQL systems, semantic search capabilities, and Agentic AI workflows. Design and implement efficient, scalable data ingestion and processing pipelines. Seamlessly integrate AI models with existing enterprise applications and APIs. Monitor, troubleshoot, and enhance the performance of AI applications. Actively participate in architectural decisions and provide mentorship to team members.

Possess at least 3 years of professional experience. Demonstrate 4+ years of experience in Python development. Showcase 2+ years of hands-on experience in AI-GenAI-RAG and LLM application development. 15 years of full-time education is required. Proficiency in Python, Object-Oriented Programming (OOPs), Design Patterns, and Unit Testing. Experience with web frameworks like FastAPI, Flask, or Django REST Framework. Solid understanding of SQL, Data Modeling, and Shell Scripting. Familiarity with data manipulation libraries such as Pandas and NumPy. Expertise in Authentication & Authorization mechanisms (JWT, OAuth, SSO). Experience integrating with LLMs (OpenAI, Gemini, Anthropic, etc.). Knowledge of Vector Search, Semantic Search, and Agentic AI Workflows. Skills in GenAI Tooling and Natural Language Interface Development. Experience developing and optimizing RAG pipelines. Familiarity with Text-to-SQL systems and libraries like LangChain, LangGraph, Sentence Transformers, and Hugging Face Transformers. Experience with embedding models and vector databases (FAISS, Qdrant, Weaviate, etc.). Ability to create and manage embeddings and vector stores. Proficiency in using Grafana for monitoring and observability. Strong command of Git and CI/CD practices.