Senior Artificial Intelligence/Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Artificial Intelligence/Machine Learning Engineer based in United States.

This is a senior platform engineering opportunity focused on accelerating enterprise adoption of Generative AI, machine learning, data science, and advanced analytics.

You will lead the automation of complex AI and data platforms, creating scalable capabilities that improve developer productivity and operational efficiency.

The role spans infrastructure provisioning, CI/CD, governance, observability, testing, deployment automation, and AI workload enablement.

You will work at the intersection of cloud engineering, DevSecOps, Infrastructure-as-Code, distributed computing, and emerging agentic AI technologies.

Your work will help engineering and data teams onboard faster, operate more reliably, and deliver AI solutions securely at enterprise scale.

You will collaborate with platform engineers, architects, data scientists, security teams, and business stakeholders on high-impact initiatives.

This role offers the opportunity to shape reusable automation frameworks and establish engineering practices for the next generation of AI platforms.

Accountabilities:

• 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.
• Collaborate with architecture, security, governance, engineering, and business stakeholders to ensure solutions meet enterprise standards, security requirements, and compliance expectations.
• Conduct technical design reviews, automation assessments, and code reviews while establishing reusable engineering standards and best practices.
• Provide technical leadership, mentorship, and guidance to engineering teams adopting automation-first development and operational approaches.
• Support strategic AI and analytics initiatives, including financial crime, AML analytics, and broader enterprise AI platform adoption efforts.

Requirements:

• 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, including storage and compute separation, interactive development environments, virtual environments, containers, Jupyter, VSCode, and developer productivity tooling.
• 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.
• Solid understanding of metadata management, data lineage, governance frameworks, and semantic-layer concepts within enterprise AI and data platforms.
• Experience designing scalable cloud-native solutions using distributed computing and modern platform engineering principles.
• Experience automating Kubernetes, containerized, YARN, serverless, and distributed processing environments.
• Experience designing and supporting event-driven architectures using Kafka or comparable streaming technologies.
• Working knowledge of agentic AI architectures, MCP frameworks, APIs, workflow automation, and enterprise AI enablement platforms.
• Strong Python development skills for automation, orchestration, scripting, tooling, and operational engineering.
• Knowledge of cloud engineering fundamentals, including networking, infrastructure management, security, resilience, scalability, and cost optimization.
• Experience implementing observability solutions covering logging, monitoring, tracing, alerting, automation, and operational dashboards.
• Ability to communicate effectively with engineers, architects, product owners, and business stakeholders with varying levels of technical expertise.
• Experience with Generative AI platforms, AI governance, model management, GitOps, DevSecOps, Reliability Engineering, and platform engineering is highly desirable.
• Familiarity with AML, financial crime, fraud detection, risk analytics, banking platforms, or enterprise financial services environments is a plus.
• Core technical skills include Python, Generative AI/LLMs, RAG, Agentic AI, MCP, Data Engineering, Kubernetes, Containers, Cloud Engineering, CI/CD, DevSecOps, Kafka/Event Streaming, APIs, Microservices, and AI Platform Engineering.

Benefits:

• Opportunity to work alongside experienced technology professionals in a collaborative, open-door environment.
• Exposure to large-scale, globally impactful technology initiatives and complex enterprise challenges.
• Internal learning opportunities, including meetups, conferences, workshops, and technical events.
• Access to Udemy and language-learning resources.
• Company-supported professional certifications and continued skills development.
• Opportunities for internal mobility across different technology domains and projects.
• Healthcare coverage according to applicable benefit plans.
• Basic life insurance.
• Short-term and long-term disability insurance.
• Opportunity to work with cutting-edge AI, cloud, automation, and distributed computing technologies.
• Career growth through technically challenging projects and opportunities to expand expertise.