Machine Learning Operations Engineer

Job Title: Machine Learning Engineer

Duration: 12 Months (Remote) Must be able to work Pacific Time Zone hours

Location: (Remote)

Job Summary:

We are seeking a highly skilled and experienced Machine Learning Engineer to design, deploy, and maintain production-grade machine learning systems that are scalable, reliable, and aligned with business strategies. This role requires expertise in end-to-end ML infrastructure development, collaboration with cross-functional teams, and strong leadership in advancing ML/GenAI model frameworks.
• Key Responsibilities:Design, implement, and maintain scalable machine learning infrastructures using cloud platforms (AWS, GCP, Azure) or on-premises systems.
• Develop and optimize CI/CD pipelines for machine learning models, automating testing and deployment processes.
• Build robust AI pipelines for data ingestion, preprocessing, search, and retrieval.
• Monitor and log model performance, system health, and anomalies using state-of-the-art monitoring solutions.
• Ensure security and compliance standards are met for ML systems, including data protection and privacy regulations.
• Lead the development and deployment of ML/GenAI models, including large language models (LLMs), ensuring alignment with business goals.
• Collaborate with data scientists, data engineers, analytics teams, and DevOps to deliver optimized solutions.
• Maintain clear and detailed documentation for ML Ops workflows, processes, and configurations.
• Qualifications:Bachelor's degree in computer science, Artificial Intelligence, Informatics, or a related field (master's degree preferred).
• Experience:3+ years of relevant experience as a Machine Learning Engineer.
• Proven experience in deploying and maintaining production-grade ML models with real-time inference, scalability, and reliability.
• Technical Expertise:Proficient in cloud platforms (AWS, GCP, Azure) for ML infrastructure development.
• Expertise in CI/CD pipelines for machine learning, including automated testing and deployment.
• Skilled in data ingestion and preprocessing pipelines for AI applications.
• Familiar with tools for monitoring, logging, and tracking model performance.
• Knowledgeable about version control for ML models and compliance with data privacy standards.
• Preferred Skills:Experience with containerization tools (Docker, Kubernetes).
• Understanding of healthcare standards, regulations, and integrating ML models with Electronic Health Records (EHR) systems.
• Certifications in machine learning or related fields.