AI/ML Engineer

AI/ML Engineer

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science or AI related fields
• Proficiency in Python and frameworks such as Tensorflow, PyTorch, or Scikit-learn
• Experience deploying ML models in cloud environments like AWS, GCP or Azure
• Familiarity with containerization tools (e.g., Docker) and orchestration frameworks (e.g., Kubernetes)
• Expertise in MLOps tools and practices, including CI/CD pipelines, model versioning, and monitoring
• Strong understanding of distributed systems and scaling machine learning workflows
• Solid knowledge of machine learning algorithms, model training, and evaluation techniques
• Ability to work in cross-functional teams and communicate effectively with data scientists, engineers, and business stakeholders
• Strong debugging and problem-solving skills to address technical challenges in deployment environments.

Responsibilities

• Design and implement robust pipelines to deploy machine learning models into production environments.
• Build and optimize scalable infrastructure for machine learning workflows, including data preprocessing, model training and inference.
• Optimize models for latency, accuracy, and efficiency in real-world scenarios.
• Work closely with data scientists to transition research models into production grade solutions.
• Develop and maintain automated workflows for version control, model monitoring, and retraining.
• Collaborate with data engineering teams to ensure efficient data pipelines and availability of high-quality datasets.
• Build tools to streamline machine learning development and deployment processes.
• Ensure deployed solutions adhere to responsible AI principles, focusing on safety.

Preferred Qualifications

• Experience working with NLP, computer vision, or generative AI models is a plus.