Senior AI Development Technician

Key Responsibilities: AI Solution Design: Collaborate with stakeholders to understand business requirements and design AI solutions that meet these needs. Model Development: Develop, train, and optimize machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn. Data Engineering: Work with large datasets to clean, preprocess, and structure data for training and deployment of AI models. Proficiency with SQL and NoSQL databases is required. Software Development: Write efficient, maintainable, and scalable code in languages such as Python, Java, or C++. Implement AI algorithms and integrate them into software applications. System Integration: Integrate AI solutions with existing systems and workflows. Use APIs and other integration methods to ensure seamless functionality. Performance Optimization: Optimize AI models for performance, accuracy, and scalability. Use techniques such as hyperparameter tuning, model pruning, and quantization. Deployment: Deploy AI models in production environments using tools such as Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP). Monitoring and Maintenance: Monitor the performance of AI systems, troubleshoot issues, and implement improvements. Ensure models remain accurate and relevant over time. Research and Development: Stay current with the latest AI research and technologies. Experiment with new methodologies and tools to improve existing solutions and develop new capabilities. Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, product managers, and business analysts, to deliver high-quality AI solutions. Required Qualifications: Education: Bachelor’s degree in Computer Science, Engineering, or a related field. Experience: 3+ years of experience in AI/ML and software engineering roles. Technical Skills: Proficiency in programming languages such as Python, Java, and C++. Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Strong understanding of AI/ML algorithms and techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning. Familiarity with data processing and analysis tools (e.g., Pandas, NumPy). Knowledge of cloud platforms and services (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). Experience with version control systems (e.g., Git) and CI/CD pipelines.