AI Engineer

About This Role

The AI Engineer is responsible for designing, developing, and deploying AI-powered solutions that address real-world challenges. The role involves working closely with data scientists, software engineers, and product teams to transform complex business problems into scalable AI-driven solutions. The position focuses on building robust AI systems, optimizing machine learning models, and integrating them into production environments to ensure reliable and efficient performance.

Responsibilities

• Model Development: Design, train, and fine-tune machine learning and deep learning models for different applications.
• Data Pipeline Management: Build and maintain robust data pipelines used for model training and inference.
• Deployment & Integration: Deploy AI models into production systems while ensuring scalability, reliability, and performance.
• Optimization: Continuously improve model accuracy, latency, and efficiency through experimentation and tuning.
• Collaboration: Work with cross-functional teams to understand business needs and convert them into technical AI solutions.
• Monitoring & Maintenance: Develop monitoring processes to track model performance and detect model drift.
• Research & Innovation: Stay updated with the latest AI/ML advancements and apply relevant technologies to enhance products.
• Compliance & Ethics: Ensure AI solutions follow ethical standards, data privacy regulations, and security best practices.

Requirements and Skills

Education

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field .

Experience

• 5+ years of overall professional experience , including 2+ years in AI/ML engineering or similar roles .
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn .
• Proven experience building end-to-end AI solutions from data collection to deployment .

Technical Skills

• Strong programming skills in Python (knowledge of C++ or Java is a plus).
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Knowledge of containerization tools like Docker and Kubernetes.
• Familiarity with MLOps tools and practices .
• Knowledge of NLP, computer vision, or reinforcement learning is preferred.

Core Competencies

• Ability to prioritize work and meet commitments aligned with organizational goals and project timelines.
• Strong capability to manage complex technical challenges .
• Accountability for delivering high-quality results and meeting commitments.