Do you have excellent experience building data pipelines?Do you enjoy collaborating cross-functionally to deliver on common goals?Join us in shaping a more just world.About UsLexisNexis, a part of RELX, is a leading global provider of legal, regulatory, and business information. We help customers increase productivity and improve decision-making and outcomes. Our 10,500 experts and innovative tools help us shape a better world for our customers and communities.About the RoleAt LexisNexis we develop the legal profession's most innovative products for data analysis, visualization, and research. We use the latest techniques in AI, machine learning, and data visualization to uncover insights about judges' rulings, build forecasts of likely outcomes, and reveal critical connections in massive datasets spanning the law, news, and finance.ResponsibilitiesAs a senior machine learning operations engineer on our team, you will work on new product development in a small team environment writing production code in both run-time and build-time environments. You will help propose and build data-driven solutions for high-value customer problems by discovering, extracting, and modeling knowledge from large-scale natural language datasets. You will prototype new ideas, collaborating with other data scientists as well as product designers, data engineers, front-end developers, and a team of expert legal data annotators. You will get the experience of working in a start-up culture with the large datasets and many other resources of an established company. You will also:Develop and implement a strategy for continuous improvement of our Machine Learning Ops including versioning, testing, automation, reproducibility, deployment, monitoring, and data privacyDevelop and report on ML Ops metrics such as deployment frequency, lead time for changes, mean time to restore, and change failure rateCollaborate with data scientists, data engineers, API engineers, and the dev ops teamBuild scalable data ingestion and machine learning inference pipelinesScale up production systems to handle increased demand from new products, features, and usersProvide visibility into the health of our data platform (comprehensive view of data flow, resources usage, data lineage, etc) and optimize cloud costsAutomate and handle the life-cycle of the systems and platforms that process our dataRequirementsMasters degree in Software Engineering, Data Engineering, Computer Science or related field5 years of relevant work experienceStrong Scala and Python backgroundExperience with Apache Spark and/or RayKnowledge of AWS, GCP, Azure, or other cloud platformKnowledge of current principles and frameworks for ML OpsExperience with ML Ops technologies such as ML Flow, DVC, Grafana, DataHub, DatabricksExperience with machine learning technologies such as PyTorch, TensorFlow, AWS SagemakerExperience with CI/CD pipelines, including Jenkins or Git ActionsExperience with Docker containerization or Kubernetes orchestrationExperience in improving data security and privacy, and managing and reducing cloud costsKnowledge of API development and machine learning deploymentWork in a way that works for youWe promote a healthy work/life balance across the organization, with various flexible and remote working options available to employeesWorking with UsLexisNexis Legal & Professional is proud to be an equal-opportunity employer. We are committed to equal opportunity employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Together, we are building a diverse and inclusive workplace.Working for youWe believe in a healthy work/life balance. We know that your well-being and happiness are key to a long and successful career.About our TeamLexisNexis is a data and analytics company with 10,500 colleagues serving customers in more than 150 countries. We're one of the largest information and analytics companies on the planet. We design solutions that help our customers increase productivity, improve decision-making and outcomes, and be more successful.
