AI Platform & Automation Engineer

Role Overview

We are looking for logical freshers and early-career engineers who can demonstrate strong technical thinking and a genuine interest in AI. Your degree matters less than your ability to think sharply, solve complex problems, build, experiment, debug, and learn fast. This role involves designing and managing automated AI and data workflows, building RAG pipelines, and developing backend services to support production AI applications.

Responsibilities

• Design and manage automated AI and data workflows using n8n and Python.
• Build pipelines to extract and process information from complex, unstructured documents using Docling, LlamaParse, or similar technologies.
• Convert unstructured data into clean, structured JSON, Markdown, and AI-ready datasets.
• Build and maintain RAG pipelines, embeddings, and vector database workflows using Qdrant, Pinecone, Weaviate, Milvus, or ChromaDB.
• Integrate and orchestrate LLMs, AI agents, and third-party AI APIs.
• Develop backend APIs and services using Python, FastAPI, Flask, or Django.
• Deploy and maintain AI services using Docker, cloud infrastructure, and CI/CD pipelines.
• Support model deployment, monitoring, optimization, and production reliability.
• Collaborate with AI and software engineers to prepare high-quality datasets for model training and fine-tuning.

Requirements

• Strong Python programming and scripting skills.
• Strong logical thinking and problem-solving ability.
• Understanding of JSON, APIs, data structures, and data processing.
• Familiarity with LLMs, Generative AI, prompt engineering, embeddings & RAG.
• Knowledge of databases and vector databases.
• Understanding of REST APIs and backend development.
• Familiarity with Git and version control.
• Basic understanding of Docker, cloud platforms, and deployment workflows.

Nice to Have

• Experience with n8n or other workflow automation platforms.
• Experience with Qdrant, LangChain, LangGraph, LlamaIndex, or Hugging Face.
• Experience with Docling, LlamaParse, OCR, or document-processing pipelines.
• Familiarity with PostgreSQL, MongoDB, or Redis.
• Knowledge of Docker, Kubernetes, AWS, Azure, or Google Cloud.
• Understanding of MLOps, model serving, monitoring, and observability.

Skills

• Python
• n8n
• FastAPI
• Docker
• Qdrant