Machine Learning Engineering Specialist - AI Innovation Lab

ZS Associates - Chicago, IL

Full-time - Mid Level

Remote - Chicago, IL

Professional, Scientific, and Technical Services

About the position

The Architecture & Engineering Specialist - ML Engineering role at ZS involves working within the Scaled AI practice to create continuous business value for clients through innovative machine learning and engineering capabilities. The position focuses on collaborating with data scientists to develop advanced AI models, manage the complete ML lifecycle, and implement technical features that adhere to best practices. This role is integral to delivering high-quality solutions that drive impactful results for clients.

Responsibilities

• Design and implement technical features leveraging best practices for the 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 to validate design and implementation approaches.
• Write production-ready code that is easily testable and understood by other developers, accounting for edge cases and errors.
• Ensure the highest quality of deliverables by following architecture/design guidelines and coding best practices, including periodic design/code reviews.
• Write unit tests and higher-level tests to handle expected edge cases and errors gracefully.
• Use bug tracking, code review, version control, and other tools to organize and deliver work.
• Participate in scrum calls and agile ceremonies, effectively communicating work progress, issues, and dependencies.
• Research and evaluate 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 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.

Requirements

• 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.
• Experience with big data technologies such as Hive, Spark, Hadoop, and queuing systems like Apache Kafka/Rabbit MQ/AWS Kinesis.
• Ability to quickly adapt to new technology and innovate in creating solutions.
• Ability to independently run POCs on new technologies and document findings to share.
• Strong proficiency in at least one programming language such as PySpark, Python, Java, or Scala, along with programming basics like Data Structures.
• Hands-on experience in building metadata-driven, reusable design patterns for data pipeline orchestration and 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.

Nice-to-haves

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

Benefits

• Comprehensive total rewards package including health and well-being benefits.
• Financial planning support.
• Annual leave.
• Personal growth and professional development opportunities.
• Robust skills development programs.
• Multiple career progression options and internal mobility paths.
• Flexible work arrangements combining work from home and on-site presence.