AI Engineer

As an AI Engineer for our client, you'll be instrumental in building AI models and algorithms that power the next generation of their products and services. You'll contribute to a range of projects—including natural language processing, computer vision, reinforcement learning, and other advanced AI applications—working alongside data scientists, software engineers, and product teams to deliver solutions that support business goals.

Key Responsibilities

• AI Model Development: Design, build, and deploy machine learning models and AI algorithms for applications such as NLP, computer vision, predictive analytics, and recommendation systems
• Data Preparation: Partner with data engineers to gather, clean, and transform large datasets, ensuring model accuracy and reliability
• Model Optimization: Continuously refine and tune models to enhance performance, scalability, and accuracy
• Research & Innovation: Keep up with the latest AI and machine learning research and apply state-of-the-art techniques to complex challenges
• Cross-Functional Collaboration: Work closely with product managers, developers, and business stakeholders to ensure AI solutions meet user needs and company objectives
• Deployment & Maintenance: Put models into production, monitor their performance, and maintain their accuracy over time
• Documentation: Maintain clear documentation for models, codebases, and processes to support scalability and future enhancements

Required Qualifications

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field
• 2+ years of experience as an AI Engineer, Machine Learning Engineer, or similar role
• Solid understanding of AI/ML algorithms, data structures, and mathematical fundamentals
• Experience with ML frameworks such as TensorFlow, PyTorch, or Keras
• Proficiency in programming languages like Python, C++, or Java
• Experience deploying models on cloud platforms (AWS, Google Cloud, or Azure) is a plus

Skills

• Strong grasp of data science concepts, including supervised and unsupervised learning, deep learning, and reinforcement learning
• Ability to work with large datasets using tools like NumPy, Pandas, and SQL
• Experience with model evaluation methods and performance metrics
• Familiarity with version control (Git) and agile development practices
• Strong problem-solving and critical-thinking skills

Soft Skills

• Excellent communication skills for effective collaboration with teams and stakeholders
• Strong organizational skills and ability to manage multiple priorities
• Team-oriented mindset with a proactive approach to sharing knowledge and learning from others