Artificial Intelligence Engineer

Position Overview

We are seeking a motivated and talented AI Engineer at a fresher level to join our dynamic team. This role offers an excellent opportunity for recent graduates or early-career professionals to work with cutting-edge artificial intelligence technologies, focusing on language models, machine learning, and advanced AI techniques.

Key Responsibilities

· Design, develop, and implement AI solutions using state-of-the-art language models and machine learning techniques

· Fine-tune pre-trained language models for specific use cases and domains

· Implement and optimize Retrieval-Augmented Generation (RAG) systems for enhanced AI performance

· Work with Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication frameworks

· Develop and deploy machine learning models in local environments

· Collaborate with cross-functional teams to integrate AI solutions into existing systems

· Conduct experiments and analyze results to improve model performance

· Stay updated with the latest developments in AI research and implement best practices

· Document technical processes and maintain code quality standards

Required Qualifications

Technical Skills

· Programming Languages: Proficiency in Python (mandatory)

· Machine Learning Frameworks: Hands-on experience with PyTorch, TensorFlow, ONNX, TensorRT, and related ML libraries

· Language Models: Strong understanding of transformer architectures, fine-tuning techniques, and language model optimization

· AI Techniques: Knowledge of RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), and A2A (Agent-to-Agent) frameworks

· Statistics: Solid foundation in statistical concepts and their application in machine learning

· Local Model Deployment: Ability to set up and run ML models in local environments

Core Competencies

· Understanding of deep learning fundamentals and neural network architectures

· Experience with model training, validation, and evaluation processes

· Familiarity with data preprocessing and feature engineering techniques

· Knowledge of version control systems (Git) and collaborative development practices

· Problem-solving mindset with attention to detail

Additional Technical Skills

· Programming Languages: Experience with additional languages such as Java, C++, JavaScript, or Go

· Specialized Tools: Experience with MATLAB and R for statistical computing and data analysis

· NLP Expertise: Background in Natural Language Processing, including text preprocessing, sentiment analysis, named entity recognition, or language understanding tasks

· Computer Vision: Knowledge of computer vision techniques, image processing, or vision-language models

· Cloud Platforms: Familiarity with AWS, Google Cloud, or Azure ML services

· MLOps: Understanding of ML pipeline development and deployment practices