AI Engineer

Role overview

This remote contract opportunity is for an AI Engineer who will help improve the training of advanced AI systems. The work focuses on supplying high-quality, real-world input that influences how models learn, reason, and perform. Prior hands-on experience in artificial intelligence is not mandatory; practical domain knowledge is the key requirement.

What you will do

• Develop, refine, and harden machine learning models so they can run reliably in production settings.
• Set up and automate full ML delivery pipelines following CI/CD principles.
• Use AWS cloud services to support scalable AI infrastructure and model rollout.
• Manage container-based workloads with Kubernetes to maintain uptime and scale smoothly.
• Work closely with data scientists, engineers, and researchers to turn complex business needs into practical ML solutions.
• Assess and prepare real-world datasets and problem statements for effective machine learning use.
• Record and present findings, solution choices, and technical decisions in a clear and structured way.

Skills and qualifications

The role calls for strong machine learning knowledge, including model design, training, and deployment. Candidates should be comfortable programming in Python or Java and should have experience contributing to large-scale software engineering work. Practical knowledge of CI/CD automation, AWS-based ML systems, and Kubernetes orchestration is important. Clear written and spoken communication is also essential, especially for technical documentation and teamwork. The ability to break down and solve complex, real-world data problems is expected.

Preferred background

Experience with deep learning tools such as TensorFlow or PyTorch will be an advantage. Exposure to fast-moving startup or high-growth environments is also preferred, along with publication history or contributions to open-source AI projects.

Additional information

This position is listed as a contract role and is remote.

Responsibilities

• Create and improve machine learning models that are suitable for production use.
• Automate the full machine learning workflow using CI/CD practices.
• Build on AWS services to support scalable deployment and AI infrastructure.
• Run and coordinate containerized ML jobs through Kubernetes.
• Partner with technical teams to convert business problems into machine learning approaches.
• Prepare, evaluate, and structure real-world data for ML applications.
• Share technical decisions and outcomes clearly through documentation and communication.

Requirements

• Hands-on knowledge of machine learning methods, model development, and deployment.
• Strong coding ability in Python or Java, ideally with large-scale software engineering exposure.
• Practical experience with CI/CD processes and automation tooling.
• Working knowledge of AWS services used in ML and data pipelines.
• Advanced Kubernetes skills for managing containerized workloads.
• Strong written and verbal communication skills for collaboration and documentation.
• Ability to analyze, structure, and solve difficult real-world data challenges.