Senior GenAI Platform Automation Engineer

Ciklum

United States

On-site

USD 180,000 - 240,000

Full time

14 days+

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Benefits offered by this job

Healthcare

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Disability Insurance

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Job summary

Ciklum is seeking a Senior Artificial Intelligence/ Machine Learning Engineer to lead automation initiatives for GenAI, Data Science, and Analytics across enterprise platforms in the USA. You will design scalable IaC and CI/CD pipelines, automate environment provisioning, governance, and observability, and collaborate with cross-functional teams to accelerate AI/analytics workloads.

The role requires deep expertise in platform automation, cloud-native tech, IaC, and DevSecOps, with a focus on

Qualifications

Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related technical field.
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 supporting AI/ML, Data Science, Data Engineering, and advanced analytics workloads.
Strong expertise in automation frameworks, DevOps methodologies, CI/CD pipelines, Infrastructure-as-Code, and software delivery lifecycle automation.
Deep understanding of modern open-source Generative AI and Data Science platform architectures including storage and compute separation, interactive development environments, virtual environments, containers, Jupyter, VSCode, and developer productivity tooling.
Hands-on experience implementing enterprise CI/CD automation using Atlassian ecosystem tools including Bitbucket, Bamboo, Jira, and Confluence.
Experience designing and implementing Infrastructure-as-Code solutions using Terraform and cloud-native automation frameworks.
Strong understanding of metadata management, data lineage, governance frameworks, and semantic layer concepts supporting enterprise AI and data platforms.
Experience building scalable cloud-native solutions utilizing distributed computing architectures and modern platform engineering principles.
Experience automating deployments and operations for Kubernetes, containerized, YARN, serverless, and distributed processing environments.
Experience designing and supporting event-driven architectures leveraging technologies such as Kafka and streaming data platforms.
Working knowledge of agentic AI architectures, MCP frameworks, API integrations, workflow automation, and enterprise AI enablement platforms.
Strong Python development experience for automation, orchestration, scripting, tooling, and operational engineering use cases.
Knowledge of cloud engineering principles including networking, infrastructure management, security, resilience, scalability, and cost optimization.
Experience implementing observability frameworks including logging, monitoring, tracing, alerting, automation, and operational dashboards.
Ability to communicate effectively with engineers, architects, product owners, and business stakeholders across varying technical levels.

Responsibilities

Lead automation initiatives for enterprise GenAI, Data Science, Metadata, Data Quality, Event Streaming, and Analytics platforms
Design and implement self-service automation capabilities that streamline onboarding, environment provisioning, deployment, governance, monitoring, and operational workflows
Build automated platform services supporting the complete AI and analytics lifecycle including data preparation, experimentation, model training, deployment, inferencing, observability, and lifecycle management
Develop Infrastructure-as-Code (IaC) solutions using Terraform and related automation frameworks to enable repeatable, scalable, and compliant infrastructure deployments
Design and implement enterprise CI/CD pipelines, automated testing frameworks, deployment automation, and release management processes using Atlassian and related DevOps toolchains
Partner with platform engineering and cloud teams to automate Kubernetes, container, serverless, and distributed computing environments
Build automation solutions supporting agentic AI applications, MCP-enabled services, event-driven architectures, and enterprise AI workflows
Drive operational excellence through platform monitoring, observability, automated remediation, performance optimization, and reliability engineering practices
Collaborate with architecture, engineering, governance, security, and business stakeholders to ensure platforms meet enterprise standards and compliance requirements
Conduct technical design reviews, automation assessments, code reviews, and establish engineering best practices across teams
Provide technical leadership, mentorship, and guidance to engineering teams adopting automation-first development and operational practices
Support key business initiatives including Consumer AML Analytics and other strategic AI platform adoption efforts