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.

Key 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.

Required Technical Skills

·        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.

Secondary Skills

·        Curiosity to explore Agentic AI frameworks and autonomous reasoning systems.

·        Analytical mindset with a focus on debugging, optimization, and scalability.

·        Strong communication and documentation skills for collaborative workflows.

·        Innovative and research-driven approach toward new AI architectures.