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.

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.