AI/ML Engineer

Job Description

The AI Ops role is responsible for the design and implementation of productionized Artificial Intelligence (AI) solutions to solve business problems. You will work closely with our data science teams and other stakeholders to enable the integration of AI/ML models into business processes. These solutions will be scalable, resilient, and secure. This role will maintain CI/CD pipelines, productionize models (ML/DL/LLM), and develop integration code needed to deploy AI solutions. AI Solution Development (35%) Lead the design and implementation of productionized models (ML/DL/LLM) solutions that are scalable, resilient, and secure. Evaluate and optimize the data science methodology needs while meeting the non-functional requirements of the business process. Research and Development (25%) Keep up-to-date with the latest technology trends. Research new ways for applying AI solutions in our business. Model Monitoring and Maintenance (20%) Monitor and maintain the performance of deployed models. Monitor production performance and provide recommendations for maximizing ML/LLM configurations and performance. Assist tool/platform admins with health and maintenance of the ecosystem. Deployment Pipeline Management (10%): Develop and maintain release pipelines for data science teams that support Continuous Integration (CI), Continuous Deployment (CD). Automate build and deployment procedures to streamline delivery. Schedule and validate all production deployments. Partner with Release Management, Infrastructure, DevOps, etc. to ensure a smooth and successful deployment. Team Leadership and Collaboration (10%) Lead and mentor team of AI Ops developers. Promote AI/ML Ops practices within the Data Science community. Collaborate with different teams to implement models and monitor outcomes. Drive cross-functional projects. Provide updates to stakeholders on status of deployments and any risks & issues. Minimum Qualifications: ?High School Diploma or GED ?4 to 7 years of experience in AI Ops, ML engineering, data science, data engineering, DevOps, analytics, or related fields Preferred Qualifications: ?Bachelor s degree in Statistics, Mathematics, Engineering, Data Science, Computer Science. ?4 to 7 years of professional experience in AI Ops, ML engineering, data science, data engineering, DevOps, analytics, or related fields Skills: Problem Solving Agile / Product oriented development Data science / statistical modeling techniques AI Ops / ML Ops / ML Engineering Designing scalable Model as a Service solutions Research into new technologies, tools, techniques Can work on multiple concurrent initiatives Team leadership, mentorship, coaching Knowledge and Abilities: Python, SQL, R, Scala Predictive modeling, machine learning, data engineering, data operations ML engineering frameworks, including ML Flow, Airflow, Langchain, Langfuse, LLM Guard, etc. Cloud based tools, including Kubernetes, Docker, AWS, Azure, APIMs Understanding of Machine Learning techniques and algorithms Deploying API endpoints Troubleshooting and diagnosing production deployments of AI solutions