Lead AWS Platform Engineer

Responsibilities

• Enable, architect, and support secure, governed, and scalable AI/GenAI platform capabilities that accelerate enterprise AI adoption across the Bank.
• Design, implement, and maintain AI enablement environments, services, frameworks, and reusable architectural patterns to support rapid experimentation, proof of concepts (POCs), and production readiness.
• Architect, provision, and maintain AWS cloud-based AI platform infrastructure and shared services using Infrastructure as Code (IaC), automation pipelines, and cloud engineering best practices to ensure scalability, resiliency, and operational efficiency.
• Enable and support AWS AI/ML and cloud-native services such as Amazon Bedrock, SageMaker, S3, Lambda, API Gateway, and related platform capabilities within secure and governed enterprise environments.
• Partner with cross-functional teams including engineering, infrastructure, security, risk, data, and business stakeholders to onboard and enable AI solutions aligned with enterprise governance and technology standards.
• Evaluate, enable, and operationalize emerging AI/ML and Generative AI technologies, services, and platforms for enterprise use cases and strategic initiatives.
• Establish and maintain AI platform standards, reference architectures, guardrails, and best practices for secure and responsible AI development and deployment.
• Support the enablement and integration of structured and unstructured data sources, cloud-native AI services, APIs, and enterprise platforms to facilitate scalable AI solution delivery.
• Stay current with emerging AI/ML technologies, AWS cloud technologies frameworks, and methodologies.
• Contribute to establishing best practices for AI exploration and deployment.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics, or a related technical discipline; advanced degree preferred but not required.
• 6+ years of experience in cloud platform engineering, AI/ML enablement, platform architecture, or enterprise technology solution delivery within secure and governed environments.
• Experience enabling, supporting, or operationalizing AI/ML and Generative AI platforms, cloud-native services, and enterprise technology capabilities in AWS environments.
• Hands-on experience with AWS cloud platform services, infrastructure automation, DevOps/IaC practices, and platform enablement frameworks supporting scalable AI and data solutions.
• Experience collaborating with cross-functional engineering, infrastructure, security, risk, and business teams to deliver enterprise technology and AI enablement capabilities aligned with governance and operational standards.
• Strong understanding of AWS cloud platform engineering concepts, including provisioning, configuration, automation, monitoring, and operational support of scalable cloud-native environments.
• Experience enabling and supporting AWS AI/ML and GenAI services such as Amazon Bedrock, SageMaker, Lex, Lambda, API Gateway, S3, IAM, CloudWatch, and related cloud platform capabilities.
• Proficiency in Infrastructure as Code (IaC), CI/CD automation, and DevOps practices using tools such as Terraform, Jenkins, CloudFormation, GitHub, or similar platform engineering technologies.
• Strong programming and scripting skills with proficiency in Python and familiarity with SQL, APIs, automation scripting, and cloud integration patterns.
• Experience enabling Generative AI capabilities including prompt engineering, Retrieval-Augmented Generation (RAG), model orchestration, evaluation frameworks, tool integration, and agentic AI enablement patterns.
• Knowledge of AI orchestration and frameworks such as LangChain, LlamaIndex, MCPs, vector databases, and enterprise AI integration architectures.
• Experience designing reusable frameworks, reference architectures, guardrails, and operational standards for secure and governed AI platform enablement.
• Familiarity with enterprise security, risk, governance, access management, and compliance considerations related to cloud and AI platform operations.
• Experience supporting AI/ML experimentation, proof of concepts (POCs), and platform enablement activities within controlled enterprise environments.
• Working knowledge of machine learning concepts, model lifecycle management, observability, and AI evaluation methodologies supporting responsible AI adoption.
• Engineering Practices: Proficiency with version control systems (Git/GitHub); Understanding of CI/CD pipelines and DevOps practices; Experience with containerization and Infrastructure as Code (Docker, Terraform); Knowledge of data structures, algorithms, and software design principles; Experience designing observability systems for AI applications.
• Business and Soft Skills: Collaborates effectively with peers and cross-functional teams; Applies sound judgment and resolves issues using established guidelines; Excellent written and verbal communication skills; Strong analytical thinking and problem-solving abilities; Ability to manage time effectively and prioritize competing demands; Self-motivated with demonstrated capacity to work independently; Experience working in Agile environments; Proficiency with Microsoft Office suite (Word, Excel, PowerPoint).