AI Engineer

About the Role

As an AI Engineer, you will apply your expertise in machine learning, cloud infrastructure, and MLOps to help train and improve next-generation AI systems.

Your work will contribute real-world AI engineering knowledge, production machine learning practices, scalable deployment strategies, and infrastructure expertise that help advanced AI models better understand modern AI development workflows.

No prior experience working on AI training projects is required. Strong machine learning and engineering expertise are the primary qualifications.

Key Responsibilities

Machine Learning Development

• Design, build, and optimize machine learning models for production environments
• Develop solutions for real-world business and data challenges
• Evaluate, preprocess, and structure datasets for effective machine learning applications

MLOps & Automation

• Build and automate end-to-end machine learning pipelines
• Implement CI/CD workflows for model deployment, testing, validation, and monitoring
• Improve reliability and reproducibility across ML workflows

Cloud Infrastructure

• Leverage AWS services to support model training, model deployment, data pipelines, and scalable AI infrastructure
• Design cloud-native solutions that support production-grade AI systems

Kubernetes & Scalability

• Deploy and orchestrate containerized machine learning workloads using Kubernetes
• Ensure high availability, scalability, reliability, and operational efficiency across production environments

Cross-Functional Collaboration

• Work closely with Data Scientists, Software Engineers, Researchers, and Technical Stakeholders
• Translate complex business requirements into practical machine learning solutions
• Communicate technical decisions, findings, and recommendations clearly

Required Skills & Qualifications

• Strong expertise in Machine Learning, Model Development, and Model Deployment
• Strong programming skills in Python, Java, or similar production languages
• Experience with CI/CD pipelines, automation tools, and MLOps practices
• Hands-on expertise with AWS cloud services
• Advanced knowledge of Kubernetes and container orchestration
• Strong understanding of data processing, feature engineering, and production AI systems
• Excellent written communication, verbal communication, and technical documentation
• Demonstrated ability to solve complex data and machine learning challenges

Preferred Qualifications

• Experience with deep learning frameworks such as TensorFlow or PyTorch
• Experience working in startups, high-growth technology companies, or AI-focused organizations
• Contributions to open-source AI projects, research publications, or machine learning communities

Additional Information

This opportunity allows experienced AI engineers to contribute machine learning, cloud infrastructure, and MLOps expertise toward improving advanced AI systems while helping shape the future of AI-powered technologies.