Machine Learning Architect

Published on Aug 03, 2021

An Outsource company with offices in Kyiv, Ukraine; Lviv, Ukraine; Kharkiv, Ukraine; Dnipro, Ukraine.

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5+ years
Job Type:
Data Engineering
Deep Learning
Data Science & AI

What You Will Do Responsibilities

• Bringing your deep expertise in cloud architecture/DevOps to analyze and recommending enterprise-grade solutions for operationalizing AI/ML Analytics
• Developing end-to-end (Data/Dev/ML) Ops pipelines based on an in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably
• Prototyping and demonstrate solutions for clients in customer environments
• Using your judgment to craft solutions to complex problems or seeking guidance as needed
• Developing assets, accelerators, and thought capital for your practice
Staying current on new products that clients could use
• Communicating use cases, requirements, and expectations with stakeholders
• Guiding Engineering and Data Science teams on ML systems production lifecycle
• Guiding Data Science teams on model operationalization strategies
• Educating Product teams on best practices for putting ML systems in production

What You Should Bring

• MS degree in computer science or related field
• 5+ years of relevant background including 2+ years of design and implementation of enterprise-scale AI/ML solutions in AWS, GCP, or Azure clouds
• Designing sustainable architectures, performing trade-off analysis of different architecture tactics and patterns, and applying proven architecture design approaches and methodologies
• Customer-facing experience of discovery, assessment, execution, and operations
• Hands-on expertise in ML operationalization
• Driving projects roll-outs from requirements gathering to go-live
• Kubernetes platform and its design patterns
• Strong requirements gathering and estimation
• Upper-Intermediate English level or higher


• Relevant Cloud Architecture certification from any of the three major cloud platforms (AWS/Azure/GCP)
• Pre-sales or enterprise consulting
• Building solutions with Kubeflow, MLflow, or similar
• Hadoop ecosystem and Databricks
• Workflow orchestration platforms like Airflow
• Designing and building feature stores
• Message queues and streaming platforms

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