AI/ML Engineer II

About the role

The AI/ML Engineer II is a mid-level position for individuals with professional experience in designing and implementing machine learning algorithms. This role involves developing and deploying AI/ML solutions to address complex challenges such as autonomous systems, predictive maintenance, and computer vision.

Responsibilities

• Design, implement, and optimize machine learning models for applications such as object detection, signal processing, predictive analytics, and decision-making systems.
• Develop and maintain data pipelines for collecting, preprocessing, and managing large-scale datasets.
• Identify data gaps and propose solutions to improve data quality.
• Conduct performance testing and validation of AI/ML models using rigorous evaluation metrics.
• Optimize models for accuracy, efficiency, and scalability.
• Write and deploy efficient, modular code to integrate AI/ML models into operational systems, ensuring reliability and compatibility with existing platforms.
• Test AI/ML solutions in simulated environments to evaluate performance under real-world conditions. Contribute to system-level debugging and troubleshooting.
• Collaborate with hardware engineers, software developers, and systems architects to align AI/ML solutions with mission-critical requirements.
• Document technical designs, workflows, and testing procedures for internal and external use. Share findings and best practices with team members.
• Explore and integrate emerging AI/ML frameworks, tools, and methodologies to enhance system capabilities and address new challenges.
• Develop and integrate signal processing and computer vision modules to enhance perception and decision-making capabilities.
• Conduct simulations and performance profiling of AI/ML models on CPU/GPU architectures, identifying bottlenecks.
• Execute validation and verification procedures, analyze test results, and support system compliance with safety and reliability standards.

Qualifications

• You Must Have:
• Bachelor’s degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline
• 2+ years of experience in a related field
• Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 6 years of related experience is required.
• A higher level relevant degree may substitute for experience.
• Practical experience using machine learning frameworks (e.g., TensorFlow, PyTorch) and applying core AI/ML techniques, including supervised, unsupervised, and introductory reinforcement learning methods.
• Hands-on experience implementing and evaluating ANNs, CNNs, and RNNs in small-scale or pilot projects.
• Assisted with deploying machine learning models in production or research environments.
• Proficiency in programming languages such as Python, C++, C#, or Java.
• A strong understanding of supervised and unsupervised learning techniques.
• Experience deploying AI/ML solutions in production environments.
• We Prefer:
• A Master’s degree in Artificial Intelligence, Machine Learning, or related field.
• Experience with reinforcement learning or generative AI models (e.g., GANs, Transformers).
• Working knowledge of Agile or DevOps practices in software/ML project environments.
• Hands-on experience with at least one advanced ML technique (e.g., clustering or dimensionality reduction) in coursework or projects.
• Basic experience with GPU programming (e.g., CUDA basics) or using GPUs for ML model training.
• Exposure to generative models (e.g., GANs, Transformers) or reinforcement learning frameworks.
• Experience analyzing and processing diverse datasets to extract insights.
• Familiarity with requirements gathering and basic deployment of ML systems.
• Awareness of hardware acceleration tools and edge AI concepts.