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Machine Learning Engineering Specialist

2 months ago

Chicago, United States Zs Associates Full time

Architecture & Engineering Specialist – ML Engineering

ZS's Scaled AI practice is part of ZS's rich and advanced AI ecosystem, in the Architecture & Engineering Expertise Center, focused on creating continuous business value for clients using a range of innovative machine learning, deep learning, and engineering capabilities.

Being part of Scaled AI practice allows you to collaborate with data scientists to create state-of-the-art AI models, create and use cutting-edge ML platforms, create and deploy advanced ML pipelines, and manage the complete ML lifecycle.

What You’ll Do

  • Design and implement technical features leveraging best practices for technology stack being used.
  • Collaborate with client-facing teams to understand solution context and contribute to technical requirement gathering and analysis.
  • Work with technical architects on the team to validate design and implementation approach.
  • Write production-ready code that is easily testable, understood by other developers, and accounts for edge cases and errors.
  • Ensure the highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews.
  • Write unit tests as well as higher-level tests to handle expected edge cases and errors gracefully, as well as happy paths.
  • Use bug tracking, code review, version control, and other tools to organize and deliver work.
  • Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues, and dependencies.
  • Consistently contribute in researching & evaluating the latest technologies through rapid learning, conducting proofs-of-concept, and creating prototype solutions.
  • Support the project architect in designing modules/components of the overall project/product architecture.
  • Break down large features into estimable tasks, lead estimation and can defend them with clients.
  • Implement complex features with limited guidance from the engineering lead.
  • Systematically debug code issues/bugs using stack traces, logs, monitoring tools, and other resources.
  • Perform code/script reviews of senior engineers in the team.
  • Mentor and groom technical talent within the team.

What You’ll Bring

  • At least 5+ years of relevant hands-on experience in deploying and productionizing ML models at scale.
  • Expertise in designing, configuring, and using ML Engineering platforms like Sagemaker, MLFlow, Kubeflow, or other platforms.
  • Big data experience with Hive, Spark, Hadoop, and queuing systems like Apache Kafka/Rabbit MQ/AWS Kinesis.
  • Ability to quickly adapt to new technology and be innovative in creating solutions.
  • Ability to independently run POCs on new technologies and document findings to share.
  • Strong in at least one of the programming languages - PySpark, Python, Java, Scala, etc. and programming basics - Data Structures.
  • Hands-on experience in building metadata-driven, reusable design patterns for data pipeline, orchestration, ingestion patterns (batch, real-time).
  • Experience in designing and implementing solutions on distributed computing and cloud services platforms (AWS, Azure, GCP).
  • Hands-on experience building CI/CD pipelines and awareness of practices for application monitoring.

Additional Skills:

  • AWS/Azure Solutions Architect certification with an understanding of broader AWS/Azure stack.
  • Understanding DevOps CI/CD, data security, experience in designing on cloud platforms.
  • Willingness to travel to other global offices as needed to work with the client or other internal project teams.
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