Senior Artificial Intelligence/Machine Learning Engineer

Ciklum • United States,

3d • Remote

About The Position

As a Senior Artificial Intelligence/Machine Learning Engineer, you will be part of a cross-functional development team engineering experiences of tomorrow. This role is a feature lead responsible for advancing end-to-end automation of GenAI and Agentic applications development using DevOps, cloud engineering, and operational excellence across enterprise GenAI and Data Science platforms. The position will build scalable automation frameworks for infrastructure provisioning, CI/CD, governance, observability, and AI workload deployment, enabling faster onboarding and delivery of enterprise AI solutions. Ideal candidates bring strong experience in platform automation, cloud engineering, Infrastructure-as-Code, GenAI technologies, and large-scale distributed platforms. This is a senior platform automation engineering role focused on accelerating enterprise adoption of Generative AI, Data Science, Data Engineering, and Advanced Analytics capabilities. The role will lead automation initiatives that improve developer productivity, platform reliability, operational efficiency, governance, and self-service adoption across enterprise AI and data platforms. The successful candidate will be responsible for designing, building, and operationalizing automated platform capabilities spanning infrastructure provisioning, CI/CD, environment management, governance controls, observability, testing, deployment automation, and AI workload enablement. The individual will work closely with platform engineering, cloud engineering, architecture, data science, and business teams to deliver scalable, secure, and resilient automation solutions supporting the full lifecycle of AI and analytics workloads. This role requires strong expertise in platform automation, cloud-native technologies, Infrastructure-as-Code (IaC), DevSecOps, Generative AI ecosystem tooling, and distributed computing platforms. The ideal candidate combines deep engineering expertise with a passion for automation, operational excellence, and continuous platform innovation.

Requirements

• 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

Nice To Haves

• Experience supporting enterprise Generative AI platforms, AI governance frameworks, model management, and AI operationalization initiatives
• Knowledge of AML, financial crime, risk analytics, fraud detection, or banking domain platforms
• Experience building platform automation for data governance, data quality, metadata management, and model lifecycle management
• Experience implementing GitOps, DevSecOps, Reliability Engineering (RE), and platform engineering best practices
• Familiarity with large-scale cloud environments and enterprise data platforms
• Experience creating reusable developer platforms, internal engineering tools, and self-service automation capabilities at enterprise scale
• Python
• Generative AI / LLMs
• RAG & Agentic AI
• MCP
• Data Engineering
• Kubernetes & Containers
• Cloud Engineering
• CI/CD & DevSecOps
• Kafka/Event Streaming
• APIs & Microservices
• AI Platform Engineering

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

Benefits

• Healthcare
• Basic Life Insurance
• Short and Long-term disability insurance according to the Company’s Benefit Plans
• Udemy access
• language courses
• company-paid certifications

What This Job Offers

• Job Type: Full-time
• Career Level: Senior