AWS Cloud & AI Platform Engineer

We are looking for an experienced AWS Cloud & AI Platform Engineer to design, build, and support secure, scalable, and governed AI/GenAI platform capabilities within an enterprise banking environment., * Architect and support scalable AWS-based AI/GenAI platforms.

• Build reusable AI enablement environments, frameworks, reference architectures, and platform services.
• Provision and maintain AWS infrastructure using IaC and automation.
• Work hands-on with Amazon Bedrock, SageMaker, S3, Lambda, API Gateway, IAM, and CloudWatch.
• Enable GenAI solutions involving RAG, prompt engineering, model orchestration, evaluation, tool integration, and agentic AI.
• Integrate structured and unstructured enterprise data with AI platforms.
• Establish AI governance, guardrails, security standards, monitoring, observability, and responsible AI practices.
• Support AI/ML experimentation, POCs, and production readiness.
• Partner with engineering, infrastructure, security, risk, data, and business teams.
• Evaluate emerging AI/ML and GenAI technologies for enterprise adoption.
• Develop technical documentation, architectural guidance, and operational procedures.

The ideal candidate will have strong hands-on experience with AWS cloud engineering, AI/ML platform enablement, Generative AI, Infrastructure as Code, Python, CI/CD, and enterprise AI governance. Prioritize candidates who are strong AWS platform engineers first, with credible hands-on GenAI/AI enablement experience, especially Bedrock, RAG, IaC, Python, security, and governance., * 9+ years of relevant cloud/platform engineering, AI/ML enablement, platform architecture, or enterprise technology experience.

• Strong hands-on AWS experience.
• Amazon Bedrock and/or SageMaker.
• AWS services including Lambda, S3, API Gateway, IAM, and CloudWatch.
• Python programming/scripting.
• Infrastructure as Code: Terraform and/or CloudFormation.
• CI/CD / DevOps: Jenkins, GitHub, or similar tools.
• Hands-on Generative AI / AI platform engineering experience.
• RAG and prompt engineering.
• AI/model orchestration and evaluation frameworks.
• LangChain and/or LlamaIndex.
• MCPs and vector databases.
• Agentic AI enablement/integration patterns.
• Enterprise AI risk management, security, governance, access management, and compliance.
• Experience building reusable platform frameworks, guardrails, and reference architectures.

Good to Have

• AWS Lex.
• SQL and API integration experience.
• Model lifecycle management.
• AI/ML observability and model/output evaluation.
• Experience supporting AI POCs within highly controlled or regulated enterprise environments.
• Banking or financial-services domain experience.