What are the responsibilities and job description for the AI Engineer position at EXOS IT?
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
- Work with large datasets, applying data cleaning, preprocessing, and transformation techniques. o Engineer relevant features for training robust and high-performing AI models.
- Optimize data pipelines for real-time AI processing where applicable.
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.
