Senior Artificial Intelligence/Machine Learning Engineer

JobgetherTechnology

Based in United StatesFull-TimeSenior

Salary not disclosed

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Job Details

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related technical discipline.
10+ years of hands-on experience in platform engineering, automation engineering, cloud engineering, DevOps, or large-scale distributed systems.
Proven experience building self-service enterprise platforms that support AI/ML, Data Science, Data Engineering, and advanced analytics workloads.
Strong expertise in automation frameworks, DevOps methodologies, CI/CD, Infrastructure-as-Code, and software delivery lifecycle automation.
Deep understanding of modern open-source Generative AI and Data Science platform architectures.
Hands-on experience with enterprise CI/CD automation using Atlassian ecosystem tools such as Bitbucket, Bamboo, Jira, and Confluence.
Strong experience designing and implementing Infrastructure-as-Code with Terraform and cloud-native automation frameworks.
Experience automating Kubernetes, containerized, YARN, serverless, and distributed processing environments.
Experience designing and supporting event-driven architectures using Kafka or comparable streaming technologies.
Strong Python development skills for automation, orchestration, scripting, tooling, and operational engineering.
Working knowledge of agentic AI architectures, MCP frameworks, APIs, workflow automation, and enterprise AI enablement platforms.

Lead automation initiatives across enterprise Generative AI, Data Science, metadata, data quality, event streaming, and advanced analytics platforms.
Design and implement self-service capabilities for platform onboarding, infrastructure provisioning, environment management, deployment, governance, monitoring, and operational workflows.
Build automated services supporting the complete AI and analytics lifecycle, from data preparation and experimentation through model training, deployment, inference, observability, and lifecycle management.
Develop scalable Infrastructure-as-Code solutions using Terraform and related automation frameworks to enable repeatable, secure, and compliant infrastructure deployments.
Design and maintain enterprise CI/CD pipelines, automated testing frameworks, deployment automation, and release management processes using Atlassian and related DevOps tooling.
Partner with cloud and platform engineering teams to automate Kubernetes, containers, serverless environments, distributed computing platforms, and related infrastructure.
Develop automation capabilities for agentic AI applications, MCP-enabled services, event-driven architectures, API integrations, and enterprise AI workflows.
Drive operational excellence through monitoring, observability, automated remediation, performance optimization, reliability engineering, and proactive platform management.

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