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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer - **Company:** Corebridge Financial, Inc. - **Location:** Houston, TX, United States - **Experience:** Experienced - **Contract:** Internship / Graduate position - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Cloud Computing, Cloud Engineering, Information Systems, Computer Programming, Continuous Integration, Information Engineering, Data Integration, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Standard Sql, Azure Machine Learning, Search Technologies, Software Engineering, Enterprise Data Management, AWS Cdk, Data Logging, Data Processing, Retrieval-Augmented Generation, Large Language Models, Grafana, Prompt Engineering, Generative AI, Cloudformation, Containerization, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Free and Open-Source Software, Data Management, Machine Learning Operations, Virtual Agents, Terraform, Software Version Control, Data Pipelines, Serverless Computing - **Published:** September 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=d9f307aa7663f4d2 ## About the Role * Bachelor's or master's degree in Computer Science, Data Science, Engineering, Information Systems, or a related technical field. Recent graduates are encouraged to apply. * For experienced candidates, 2+ years of relevant experience in cloud engineering, software engineering, data engineering, MLOps, AI/ML engineering, or platform engineering is preferred. * For new graduates, relevant internships, co-op assignments, research, capstone projects, or substantial hands-on coursework in AWS, AI/ML, data science, or software engineering will be considered. * Foundational experience with AWS technologies and cloud concepts, including identity and access management, networking, compute, storage, security, and monitoring. * Hands-on exposure to AI/ML concepts and tools, such as model training or inference, generative AI, large language models, embeddings, vector search, prompt engineering, or MLOps. * Programming ability in Python, Java, or a similar language, along with working knowledge of SQL, APIs, version control, and automated testing. * Exposure to infrastructure-as-code and CI/CD tools, such as AWS CloudFormation, Terraform, AWS CDK, GitHub Actions, or comparable technologies, is beneficial. * Understanding of secure engineering practices, data privacy, responsible AI, logging, monitoring, and operational reliability. * Strong problem-solving, communication, and collaboration skills, with a willingness to learn and work across multidisciplinary teams., * AWS certification, AI/ML coursework, cloud labs, hackathons, open-source contributions, or a portfolio demonstrating practical engineering work. * Exposure to Amazon Bedrock, Amazon SageMaker, container technologies, serverless services, vector databases, orchestration frameworks, or observability tools. * Experience in financial services or another regulated industry is helpful but not required. ## Description We are seeking an AI Platform Engineer to help design, build, and operate the enterprise AI/ML platform capabilities that enable secure, scalable, and reliable AI and machine learning solutions. This role will work across cloud infrastructure, data, AI engineering, and governance to support generative AI, machine learning, and agentic AI workloads on AWS. The position is open to early-career professionals with two to three years of relevant experience as well as new graduates with strong academic foundations and relevant internship or project experience., * AI/ML Platform Engineering: Build and enhance reusable platform services, development patterns, and automation that support AI/ML model development, deployment, inference, and lifecycle management on AWS. * AWS Engineering: Develop and support cloud-native solutions using relevant AWS services for compute, storage, networking, security, observability, data processing, and AI/ML, including Amazon Bedrock and Amazon SageMaker where applicable. * Generative and Agentic AI Enablement: Support the development and integration of generative AI applications, AI agents, APIs, model endpoints, prompt workflows, and retrieval-augmented generation solutions. * Platform Automation: Create infrastructure-as-code, CI/CD pipelines, deployment templates, configuration standards, and self-service capabilities that improve engineering productivity and consistency. * Data and Integration: Help connect AI/ML workloads to enterprise data platforms, APIs, event streams, and data pipelines while applying appropriate access controls and data-handling standards. * Security and Governance: Implement platform controls for identity and access management, secrets protection, encryption, logging, monitoring, auditability, model governance, and responsible AI practices. * Reliability and Operations: Build monitoring, alerting, troubleshooting, cost-management, and operational support capabilities for AI/ML services and production workloads. * Collaboration: Partner with data scientists, software engineers, data engineers, architects, security teams, and business stakeholders to translate use-case needs into scalable platform solutions. * Continuous Learning: Evaluate emerging AI/ML and AWS technologies through prototypes and proofs of concept, document findings, and contribute to platform standards and reusable engineering guidance. ## Related Videos - [AI-Augmented DevOps with Platform Engineering](https://www.wearedevelopers.com/videos/1614-ai-augmented-devops-with-platform-engineering) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Building Reliable Serverless Applications with AWS CDK and Testing](https://www.wearedevelopers.com/videos/812-building-reliable-serverless-applications-with-aws-cdk-and-testing) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Inside Bitpanda's Tech Stack: Scaling a European Fintech Leader - Markus Dorner](https://www.wearedevelopers.com/videos/1979-inside-bitpanda-s-tech-stack-scaling-a-european-fintech-leader-markus-dorner) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Got AI ideas but no money? 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