AI Engineer

April 14

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Intellect

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Intellect is a leading platform focused on enhancing mental health and wellbeing in the workplace. It offers a comprehensive, research-backed mental health platform that combines technology with human touch to provide an accessible mental health journey. The platform includes self-guided programs, access to licensed psychologists, behavioural health coaches, and crisis support, available in 30 languages across 60 countries. Intellect is designed to empower HR professionals and organizations by providing tools that improve employee wellbeing, decrease anxiety, and boost productivity. It facilitates customised mental health support, allowing organisations to tackle challenges such as low productivity and team dynamics effectively.

51 - 200 employees

🧘 Wellness

🤝 B2B

☁️ SaaS

📋 Description

• Responsible for model development, optimization, and deployment • Ensure the delivery of impactful AI solutions that improve efficiency, decision-making, and customer satisfaction • Contribute directly to the company's growth and competitiveness in the market • Collaborate closely with cross-functional teams • Leverage expertise to solve challenging problems and drive strategic initiatives that advance our mission and vision • Algorithm Development: Design, develop, and implement machine learning algorithms and models to solve complex problems and address business needs. • Data Processing and Analysis: Collect, preprocess, and analyze large datasets to extract meaningful insights and patterns, utilizing techniques such as data cleaning, feature engineering, and statistical analysis. • Model Training and Optimization: Train, validate, and optimize machine learning models to achieve high accuracy, performance, and scalability, employing techniques such as hyperparameter tuning and model selection.

🎯 Requirements

• Minimum of 2 years of proven working experience in AI/ML • Machine Learning Expertise: In-depth knowledge of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, reinforcement learning, and their practical applications. • Data Handling and Analysis: Experience with data collection, preprocessing, feature engineering, and exploratory data analysis, using tools like pandas, NumPy, or SQL for efficient data manipulation and analysis. • Model Development and Optimization: Ability to design, develop, and optimize machine learning models, including hyperparameter tuning, cross-validation, and ensemble methods, to achieve high performance and generalization. • Software Engineering Skills: Understanding of software development principles and best practices, including version control, code documentation, and software testing, to ensure the reliability and maintainability of AI solutions.