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Job Description & How to Apply Below
This role focuses on designing and building scalable machine learning platforms that power end-to-end ML and GenAI workflows for enterprise-grade systems. You will be responsible for architecting robust infrastructure that supports model training, deployment, monitoring, and lifecycle management across complex cloud environments. Working within a highly technical and collaborative engineering organization, you will bridge the gap between data science and production engineering.
The position requires deep expertise in cloud-native systems, Dev Ops practices, and ML workflows to ensure reliable, secure, and efficient platforms. You will contribute to enabling data scientists and engineers to build, deploy, and scale models seamlessly. This is a high-impact role supporting Fortune 500 clients, where platform reliability and scalability are critical to success.
Accountabilities
In this role, you will be responsible for designing, building, and optimizing end-to-end machine learning platforms that enable scalable and efficient ML operations across the organization.
Architect and implement scalable MLOps platforms supporting the full machine learning lifecycle
Design and maintain automated pipelines for data processing, model training, deployment, and monitoring
Evaluate and integrate appropriate cloud services (AWS, GCP, or Azure) for ML workloads, with a preference for Azure
Implement infrastructure as code, CI/CD pipelines, and containerized deployments using Docker and Kubernetes
Enable version control for code, data, and machine learning models to ensure reproducibility
Build tools and interfaces that support data scientists in efficiently using the ML platform
Ensure system scalability, reliability, security, and compliance with industry standards
Optimize platform performance, cost efficiency, and operational stability
Implement monitoring, logging, and alerting systems for ML workflows and infrastructure
Collaborate with data scientists, engineers, and stakeholders to align platform capabilities with business needs
Requirements
This position requires strong experience in cloud engineering and machine learning infrastructure, along with the ability to design and operate scalable ML platforms in enterprise environments.
10+ years of experience in software engineering with strong exposure to cloud-based applications
Proven experience building machine learning platforms using cloud services (Azure preferred)
Strong expertise in cloud platforms such as AWS, GCP, or Azure, including ML and data services
Hands-on experience with Dev Ops practices, CI/CD pipelines, and infrastructure as code
Proficiency in containerization and orchestration tools such as Docker and Kubernetes
Strong programming skills in Python and other ML-relevant languages
Solid understanding of ML workflows, model training, deployment, and lifecycle management
Experience with data engineering concepts including ETL pipelines and data storage systems
Strong system design skills for scalable and distributed architectures
Knowledge of security, compliance, and best practices for ML systems
Experience with monitoring, logging, and observability tools for production systems
Ability to collaborate effectively with cross-functional teams including data scientists and engineers
Benefits
Fully remote work model with flexible engagement (FTE opportunity)
Opportunity to work on enterprise-scale ML and GenAI platforms for global clients
Exposure to advanced cloud-native architectures and MLOps best practices
High-impact role shaping end-to-end machine learning infrastructure
Collaborative, cross-functional engineering environment
Competitive compensation aligned with senior-level expertise
Opportunity to work with cutting-edge AI and machine learning technologies
Career growth in large-scale platform engineering and AI systems
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools…
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