ML Engineer

at MNC

Primary Responsibilities

• Understand current state architecture, including pain points.
• Create and document future state architectural options to address specific issues or initiatives using Machine Learning.
• Innovate and scale architectural best practices around building and operating ML workloads by collaborating with stakeholders across the organization.
• Develop CI/CD & ML pipelines that help to achieve end-to-end ML model development lifecycle from data preparation and feature engineering to model deployment and retraining.
• Provide recommendations around security, cost, performance, reliability, and operational efficiency and implement them
• Provide thought leadership around the use of industry standard tools and models (including commercially available models and tools) by leveraging experience and current industry trends.
• Collaborate with the Enterprise Architect, consulting partners and client IT team as warranted to establish and implement strategic initiatives.
• Make recommendations and assess proposals for optimization.
• Identify operational issues and recommend and implement strategies to resolve problems.

Must have:

• 3+ years of experience in developing CI/CD & ML pipelines for end-to-end ML model/workloads development
• Strong knowledge in ML operations and DevOps workflows and tools such as Git, AWS CodeBuild & CodePipeline, Jenkins, AWS CloudFormation, and others
• Background in ML algorithm development, AI/ML Platforms, Deep Learning, ML Operations in the cloud environment.
• Strong programming skillset with high proficiency in Python, R, etc.
• Strong knowledge of AWS cloud and its technologies such as S3, Redshift, Athena, Glue, SageMaker etc.
• Working knowledge of databases, data warehouses, data preparation and integration tools, along with big data parallel processing layers such as Apache Spark or Hadoop
• Knowledge of pure and applied math, ML and DL frameworks, and ML techniques, such as random forest and neural networks
• Ability to collaborate with Data scientist, Data Engineers, Leaders, and other IT teams
• Ability to work with multiple projects and work streams at one time. Must be able to deliver results based upon project deadlines.
• Willing to flex daily work schedule to allow for time-zone differences for global team communications
• Strong interpersonal and communication skills