Role Overview:

We are looking for a versatile AI/ML engineer with 3 - 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 the 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 optimise data flows using Kafka for real-time processing and maintain high-performance databases (SQL and NoSQL).

- MCP Integration: Utilise 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 & Containerisation: 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).

- CI/CD: Knowledge of automated deployment pipelines (GitHub Actions or Jenkins).