MLOps Architect

Role: MLOps Architect Location: New York, NY (Remote) Responsibilities: • Architect for scalable, cost-efficient, reliable and secure MLOps solution. • Design, implement and deploy MLOps solutions in AWS. • Select and justify appropriate ML technology within AWS and Identify appropriate AWS services to implement MLOps solutions. • Design, build, and maintain infrastructure required for efficient development, deployment, and monitoring of machine learning models. • Implement CI/CD pipelines for machine learning applications to ensure smooth development and deployment processes. • Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility. • Monitor and optimize model performance in production, identifying and resolving issues proactively to ensure optimal results. • Automate repetitive tasks to improve efficiency and reduce the risk of human error in MLOps workflows. • Maintain documentation and provide training to team members on MLOps best practices, ensuring knowledge sharing and collaboration within the team. • Stay updated with the latest developments in MLOps tools, technologies, and methodologies to remain current and effective in your role. Experience: • Minimum 10+ years of experience in MLOps with a proven track record of successful deployments. • In-depth working knowledge of MLOps tools and platforms (Kubernetes, Docker, Jenkins, Git, MLflow, JupyterHub, LLM-specific tooling). • In-depth working knowledge of AWS and infrastructure as code (IaC) principles. • Strong Experience with DevOps methodologies and CI/CD pipelines such as Github Actions. • Strong understanding of machine learning pipelines, model training frameworks, and monitoring techniques. • Strong programming skills in Python • Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn. • Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment. • Experience with large language models (LLMs) and their unique operational considerations is a plus. • Excellent communication, collaboration, and problem-solving skills. • The ability to translate technical concepts into clear and concise language. • A passion for innovation and a drive to optimize ML and LLM workflows • 12+ years of experience in MLOps, DevOps, or related fields. • Hands-on experience with AWS. • Familiarity with containerization and orchestration tools like Docker and Kubernetes. • In depth Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation. • Excellent problem-solving skills and the ability to work independently as well as part of a team. • Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders. Preferred Qualifications: • AWS Certified Machine Learning – Specialty • Experience with A/B testing and model performance monitoring Apply tot his job Apply To this Job