Senior Artificial Intelligence/Machine Learning Engineer

Posted 7 Hours Ago
Hiring Remotely in United States
Remote
Senior level
Information Technology • Consulting
The Role
Lead enterprise AI platform automation across infrastructure provisioning, CI/CD, governance, observability, deployment, and lifecycle management. Build self-service capabilities for GenAI, data science, data engineering, and analytics workloads using Terraform, Kubernetes, cloud technologies, Python, and DevSecOps practices. Support agentic AI, event-driven architectures, distributed computing, and operational reliability while collaborating with engineering, architecture, governance, security, and business stakeholders. Provide technical leadership, design reviews, mentoring, and best-practice guidance.
Summary Generated by Built In

Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in USA.

We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.

About the role:

As a Senior Artificial Intelligence/Machine Learning Engineer, become a 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 across Bank of America. 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.

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

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

Desirable:

  • 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
  • Core Skills:
    • 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

What’s in it for you?

  • Strong community: Work alongside top professionals in a friendly, open-door environment
  • Growth focus: Take on large-scale projects with a global impact and expand your expertise
  • Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications
  • Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies
  • Care: Healthcare, Basic Life Insurance, Short and Long-term disability insurance according to the Company’s Benefit Plans

About us:

At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you’ll work with cutting-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. In the US, Ciklum is growing fast—inviting experienced professionals to lead digital transformation alongside Fortune 500 clients. Be part of a company where innovation and impact go hand in hand.

Explore, empower, engineer with Ciklum!

Interested already? We would love to get to know you! Submit your application. We can’t wait to see you at Ciklum.

#LI-VH1

Skills Required

  • 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
  • Experience building self-service enterprise platforms supporting AI/ML, Data Science, Data Engineering, and advanced analytics workloads
  • Expertise in automation frameworks, DevOps methodologies, CI/CD pipelines, Infrastructure-as-Code, and software delivery lifecycle automation
  • Understanding of open-source Generative AI and Data Science platform architectures, including storage and compute separation, interactive development environments, virtual environments, containers, Jupyter, and VSCode
  • Hands-on experience implementing enterprise CI/CD automation using Bitbucket, Bamboo, Jira, and Confluence
  • Experience designing and implementing Infrastructure-as-Code solutions using Terraform and cloud-native automation frameworks
  • Understanding of metadata management, data lineage, governance frameworks, and semantic layer concepts
  • Experience building scalable cloud-native solutions using 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 using 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
  • 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
  • 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, 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 at enterprise scale
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The Company
HQ: London
2,995 Employees
Year Founded: 2002

What We Do

Ciklum is a global Digital Solutions Company for Fortune 500 and fast-growing organisations alike around the world. The company is headquartered in London and has software development centres and branch offices in the United States, Spain, Switzerland, Denmark, Israel, Poland, Ukraine, Czech Republic, Slovakia, Romania, UAE and Pakistan. Ciklum builds tailored digital solutions that leverage emerging technologies for such clients as Just Eat, Flixbus, Metro Markets, EFG International, Zurich Insurance, Lottoland and others. For more information about us visit www.ciklum.com

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