Senior Artificial Intelligence/Machine Learning Engineer
- United States
نُشرت أمس
عن الوظيفة
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
Required Skills
Agentic AI
Data & GenAI Driven
Job Info
Job Identification 4391
Job Category Data Science and Machine Learning
Posting Date 08/31/2026, 03:02 AM
Locations US-Remote (Remote)
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
Required Skills
Agentic AI
Data & GenAI Driven
Job Info
Job Identification 4391
Job Category Data Science and Machine Learning
Posting Date 08/31/2026, 03:02 AM
Locations US-Remote (Remote)