Job Opening for AI Engineer Fresher (Job Code RT 1612)

Job Brief

As an AI Engineer, you will be part of a next-generation engineering team building intelligent, autonomous systems that combine Machine Learning, Large Language Models (LLMs), and Agentic reasoning frameworks. You will participate end-to-end — from data ingestion and model training to Retrieval-Augmented Generation (RAG), agent orchestration, and deployment of scalable microservices. This role demands curiosity, precision, and a passion for bridging traditional ML with modern LLM-based agentic architectures in production-grade systems.

Responsibilities

• • Design and implement RAG pipelines for large-scale data retrieval and contextual reasoning.
• • Build, train, and fine-tune ML models using Scikit-learn, TensorFlow, or PyTorch for structured and unstructured datasets.
• • Develop modular AI microservices (FastAPI/Flask) integrated with vector databases (FAISS, ChromaDB, Elasticsearch) and embedding pipelines.
• • Orchestrate multi-agent systems using LangGraph, LangChain, or Model Context Protocol (MCP) for dynamic task execution.
• • Integrate and evaluate LLM APIs (OpenAI, Azure OpenAI, Groq, Anthropic, etc.) for reasoning, generation, and workflow automation.
• • Implement data and ML pipelines (Airflow, ZenML, Dagster) for training, evaluation, and inference.
• • Develop and expose REST or WebSocket APIs for model and agent communication.
• • Apply prompt engineering and reranking strategies to improve contextual accuracy and system reasoning.
• • Work closely with data, backend, and AI engineers to deploy, scale, and monitor intelligent microservices in test/staging environments.
• • Contribute to shared AI infrastructure modules, ensuring reproducibility, maintainability, and performance.
• • Research and prototype new architectures, including multimodal RAG, structured reasoning, or fine-tuning pipelines.

Requirements

• • Curiosity to explore Agentic AI frameworks and autonomous reasoning systems.
• • Analytical mindset with a focus on debugging, optimisation, and scalability.
• • Strong communication and documentation skills for collaborative workflows.
• • Innovative and research-driven approach toward new AI architectures.
• • Eagerness to experiment, iterate, and learn from production-level systems

Skills