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