Senior Data Engineer l - ML Ops/ ML Engineer at LexisNexis South Africa

About the Role

Responsibilities

• As 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 privacy
• Develop and report on ML Ops metrics such as deployment frequency, lead time for changes, mean time to restore, and change failure rate
• Collaborate with data scientists, data engineers, API engineers, and the dev ops team
• Build scalable data ingestion and machine learning inference pipelines
• Scale up production systems to handle increased demand from new products, features, and users
• Provide visibility into the health of our data platform (comprehensive view of data flow, resources usage, data lineage, etc) and optimize cloud costs
• Automate and handle the life-cycle of the systems and platforms that process our data

Requirements

• Masters degree in Software Engineering, Data Engineering, Computer Science or related field
• 5 years of relevant work experience
• Strong Scala and Python background
• Experience with Apache Spark and/or Ray
• Knowledge of AWS, GCP, Azure, or other cloud platform
• Knowledge of current principles and frameworks for ML Ops
• Experience with ML Ops technologies such as ML Flow, DVC, Grafana, DataHub, Databricks
• Experience with machine learning technologies such as PyTorch, TensorFlow, AWS Sagemaker
• Experience with CI/CD pipelines, including Jenkins or Git Actions
• Experience with Docker containerization or Kubernetes orchestration
• Experience in improving data security and privacy, and managing and reducing cloud costs
• Knowledge of API development and machine learning deployment