TechRAQ - Artificial Intelligence/Machine Learning Engineer - Python Programming

Role Overview :

We are looking for a versatile AI/ML Engineer with 3 to 5 years of experience to join our team. You will be responsible for the end-to-end development of AI-driven products. From building robust backends to architecting complex Agentic workflows and RAG systems, you will bridge the gap between cutting-edge AI research and enterprise-grade production software.

Our core projects focus on intelligent document processing, and AI Agents along with Model Context Protocol (MCP).

Key Responsibilities :

- End-to-End AI Development : Design, develop, and deploy backend architectures for AI applications, ensuring seamless integration with production environments.

- Advanced RAG & Document Intelligence : Build enterprise-scale Retrieval-Augmented Generation (RAG) pipelines and OCR solutions for complex document processing.

- Agentic Workflows : Architect and implement autonomous AI agents and multi-agent systems using LangChain/LangGraph to solve multi-step business problems.

- Data Strategy : Manage and optimize data flows using Kafka for real-time processing and maintain high-performance databases (SQL and NoSQL).

- MCP Integration : Utilize and extend the Model Context Protocol (MCP) to connect AI models with secure, local, or remote data sources.

Technical Requirements :

Core AI & Machine Learning :

- Generative AI : Deep understanding of LLMs, prompt engineering, and fine-tuning. (Gemini and OpenAI)

- Agentic AI : Hands-on experience building autonomous agents and complex workflows (LangChain, LangGraph, or CrewAI).

- RAG : Expertise in vector databases, embedding models, and semantic search.

Backend & Data Processing :

- Languages : Production grade Python Programming.

- Data Processing : Pandas, NumPy, PyTorch

- Databases : Proficiency in PostgreSQL, MySQL, MongoDB, and Redis.

- Streaming : Experience with Kafka for event-driven architectures.

DevOps & Tools :

- Version Control & Containerization : Experienced with Git and Docker.

- Deployment : Experience building and maintaining scalable APIs (FastAPI/Flask).

Optional But Preferred Qualifications :

- Cloud Platforms : Experience with AWS (SageMaker, S3, Lambda, EC2, ECS) or Azure (Blob Storage, Functions, VMs, ACI).