.The MLOps Engineer 4 is responsible for designing, developing, and deploying scalable end-to-end machine learning solutions with an emphasis on Generative AI systems.
This position will build and maintain impactful production-grade ML systems.
The MLOps Engineer 4 will be skilled in both model development and operational deployment, ensuring the reliability and scalability of advanced ML applications.Responsibilities include:Develop and automate processes for deploying machine learning models to production environments, ensuring that models are accessible and performant in world settings.Develop/maintain a scalable ML Platform providing inference and tuning services.Implement security protocols for data and model handling and design solutions that can scale with increased data volume and usage.Create, select, and test features that improve model performance and align with business needs, often through domain knowledge and statistical analysis.Work closely with product, backend, and frontend teams to build seamless integrated software solutions.Facilitate the collection and analysis of feedback data for continuous improvement.Operate as a trusted advisor on issues and trends; provide general consulting services leveraging expertise and significant best practice knowledge.Operate as an innovative thought leader; contribute significantly to the overall growth and quality of the department through knowledge sharing and coaching on current best practices and market trends.Mentor, coach, train, and provide feedback to other team members; provide feedback to leadership on abilities of the team.Comply with all corporate and departmental privacy and data security policies and practices, including but not limited to, Hyland's Information Systems Security Policy.What will make you successful:Bachelor's degree or equivalent experience.Proven experience in ML engineering, MLOps, and/or LLMOps.Hands-on experience with ML platform frameworks (e.G., MLflow) and ML frameworks (e.G., PyTorch, TensorFlow).Proficiency in orchestrating data and ML pipelines using tools like Metaflow, Airflow, Dagster, Prefect, or AWS Glue.Strong programming skills in Python and SQL with experience in at least one data processing framework (e.G., Spark, Flink, Kafka).Familiarity with both relational and non-relational databases, vector databases, and graph databases (e.G., PostgreSQL, MongoDB, Pinecone, ElasticSearch).Experience with monitoring and observability tools (e.G., Datadog, Grafana, Prometheus).Proficient in cloud services, particularly AWS, and infrastructure management tools like Terraform and Docker.Ability to package and deploy code in cloud production environments.Excellent collaboration skills applied successfully within the team as well as with all levels of employees in other areas.Excellent critical thinking and problem-solving skills.Hands-on experience with AWS Bedrock and Sagemaker