AI Engineer (Fresher)

Role Overview

As an AI Engineer, you’ll 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’ll 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 and modern LLM-based agentic architectures into 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

• Strong understanding of RAG architecture — document chunking, embedding, retrieval, reranking, and synthesis.
• Proficiency in Python, including libraries like Pandas, Numpy, and Scikit-learn.
• Experience with FastAPI or Flask for microservice and API development.
• Hands-on with LangGraph, LangChain, or MCP for multi-agent orchestration.
• Familiarity with vector databases (FAISS, ChromaDB, Weaviate, Elasticsearch).
• Good Working knowledge of ML/DL frameworks (TensorFlow, PyTorch).
• Understanding of model serving via REST APIs, batch jobs, or pipelines.
• Exposure to containerization (Docker) and scalable architecture patterns.
• Familiarity with Git, MLflow, or DVC for versioning, tracking, and collaboration.
• Awareness of cloud AI/ML services (Azure ML, AWS SageMaker, GCP Vertex AI).
• Understanding of prompt evaluation, retrieval accuracy metrics, and contextual reasoning benchmarks.

Skills

• Python
• PyTorch
• TensorFlow
• LangChain
• FastAPI