Machine Learning Engineer

Key Skills

Agile methodologies, AzureML, Bash scripting, Cloud computing, Containerization, Databricks, Docker, Git, Jenkins, Kubernetes-K8s, Large scale data processing, Maven, MLflow, MLOps tools, Python Programming, Shell Scripting language, SonarQube, SQL

Job Description

We are looking for a highly capable machine learning engineer to optimize our machine learning systems. You will be evaluating existing machine learning (ML) processes, performing statistical analysis to resolve data set problems, and enhancing the accuracy of our AI software's predictive automation capabilities. ML Engineer helps increase the speed of delivery, improve the quality of code, and streamline processes in support of productionalizing models. The ML Engineer is responsible for identifying a production method to establish model code automation to be deployed to production systems and are operational with low latency. He or she is responsible for infrastructure implementation tools such as code and support services in multiple clouds, along with metrics and access to information

Duties & Responsibilities:

• Developers to assess existing problems and reach process improvement solutions in the ML pipeline
• Identify bottlenecks in the coding development and implementation process including packaging of code to reduce DS engineering time
• Participate in planning delivery time, code quality, and process efficiency improvement in output
• Execute the plan by creating coding standards and automating processes for the organization
• Perform daily tasks such as setting up development and production environments, monitoring reporting metrics and incidents
• Develop and maintain knowledge of platform configuration management and troubleshooting
• Actively involved in deploying application devices in appropriate environments through supported technologies and infrastructure
• Coordinate with Data Scientists, Data Engineers, and technical members of the organization to understand their main challenges in the process of coding and development specific to ML pipelines
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress 

Qualifications

Knowledge, Skills and Abilities: 

• Excellent coding and scripting skills (Bash, Shell) and experience implementing large-scale cloud architectures using modern cloud-based container platforms (PaaS/SaaS)
• Knowledge in the deployment of ML Pipelines using Azure ML, Data Bricks (MLFlow)
• Test, build, and deploy ML models in Production akin to DEV Ops but specifically
  for ML code through infrastructure including Databricks
• Knowledge of cloud infrastructure (Azure) and management tools for IT components, storage, networking, caching, etc.
• Experience with containerization, Docker, Kubernetes
• Continuous integration experience and related tools such as Jenkins, Hudson, Maven, Ant, Git, Sonar, etc.
• Strong understanding of Agile methodologies
• Experience as an MLOps Engineer or SRE is preferred in an agile cross-functional team
• Strong understanding of Agile methodologies
• Experience as an MLOps Engineer or SRE is preferred in an agile cross-functional team

Work Experience and/or Education: 

• Bachelor’s degree in computer science, data science, mathematics, or a related field
• Master’s degree in computational linguistics, data analytics, or similar will be advantageous
• At least four years of experience as a machine learning engineer