Requirements and responsibilities
Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.Key Responsibilities:Design, build, and optimize robust machine learning models for production environments.Implement and automate end-to-end ML pipelines using CI/CD best practices.Leverage AWS services for scalable AI infrastructure and model deployment.Orchestrate containerized workloads using Kubernetes to ensure high availability and seamless scalability.Collaborate with data scientists, engineers, and researchers to translate complex business problems into actionable ML solutions.Evaluate, preprocess, and frame real-world data problems for effective machine learning applications.Document and communicate findings, solution approaches, and technical decisions with clarity and precision.Required Skills and Qualifications:Proven expertise in machine learning algorithms, model development, and deployment.Strong programming skills, ideally in Python or Java, with experience in large-scale software engineering projects.In-depth experience with CI/CD workflows and automation tools.Hands-on expertise with AWS cloud services for ML applications and data pipelines.Advanced knowledge of Kubernetes for orchestrating containerized ML workloads.Exceptional written and verbal communication skills, with a focus on clear technical documentation and team collaboration.Demonstrated ability to scope, structure, and solve complex, real-world data challenges.Preferred Qualifications:Familiarity with deep learning frameworks such as TensorFlow or PyTorch.Experience working in high-growth or startup environments.Strong publication record or contributions to open-source AI projects.Required Skills: Machine Learning, CI/CD, AWS, Kubernetes
Optionally, you can add more information later (benefits, pre-screening questions, etc.)