Job description

What You Will Do: Machine Learning & AI Model Development • Design and train machine learning models for classification, regression, clustering, and recommendation systems. • Develop deep learning models using frameworks like TensorFlow or PyTorch for applications such as image recognition, NLP, and speech processing. • Optimize AI models for performance, accuracy, and efficiency. Natural Language Processing (NLP) • Implement NLP solutions for sentiment analysis, chatbots, document processing, and text summarization. • Work with pre-trained models like GPT, BERT, and LLaMA, fine-tuning them for specific use cases. o Develop AI-driven automation for extracting insights from unstructured data sources. AI Model Deployment & Infrastructure • Deploy AI models in production using cloud services (AWS, Azure, GCP) or on-premises solutions. • Develop APIs and microservices to integrate AI models with business applications. • Monitor and maintain deployed models, retraining them as needed to ensure continued performance. Data Engineering & Feature Engineering • Optimize data pipelines for real-time AI processing where applicable. • Work with large datasets, applying data cleaning, preprocessing, and transformation techniques. o Engineer relevant features for training robust and high-performing AI models. AI Research & Innovation • Stay up to date with the latest AI advancements and research papers. • Experiment with generative AI, reinforcement learning, and emerging AI technologies. • Explore ways to leverage AI for solving complex business challenges. What You Have Done: • Excellent organizational, written, and verbal communication skills. • Minimum 2 years of experience in AI development, machine learning, or a related field. • Strong programming skills in Python (preferred), with experience using TensorFlow, PyTorch, or Scikit-learn. • Experience working with cloud-based AI services such as AWS SageMaker, Azure AI, or Google Vertex AI. • Understanding of machine learning concepts, feature engineering, and model optimization techniques. • Familiarity with MLOps practices, including model deployment and monitoring. • Ability to work with structured and unstructured data for AI model training and evaluation. • Experience working with large language models (LLMs) and fine-tuning AI models. • Understanding of ethical AI, bias mitigation, and model explainability. • Experience integrating AI solutions with APIs and business applications. • AI-related certifications such as AWS Certified Machine Learning - Specialty, Microsoft AI Engineer Associate, or equivalent are a plus.

Requirements