Principal AI Platform Engineer

As a Principal AI Engineer, you will play a crucial role in defining and driving the technical direction for the systems that power our AI products. Your responsibilities will include: - Architecting and scaling the AI platform and services to support the assessment of Environmental, Social, and Governance (ESG) performance of companies by investors worldwide. - Designing and scaling LLM and ML inference platforms, including routing, caching, model lifecycle, and API standards. - Establishing production patterns for RAG, retrieval systems, vector databases, and knowledge pipelines while setting best practices for quality and relevance. - Leading the development of platform-level capabilities such as orchestration frameworks, event-driven services, and reusable microservice components with a Python-first approach. - Defining and enforcing SLOs, reliability standards, observability practices, and incident response playbooks for AI services. - Driving cost governance for AI, including infrastructure scaling, caching, batching, and evaluation-driven deployment decisions. - Building robust CI/CD and release strategies for AI systems and automating deployments on AWS. - Providing technical mentorship and influencing design decisions across teams through design reviews and architecture forums. - Evaluating new technologies and guiding their adoption with a pragmatic lens. Qualifications required for this role include: - Expert-level programming skills in Python for developing services, APIs, pipelines, and platform components. - 9+ years of experience in AI engineering, MLOps, backend/platform engineering, or related roles with demonstrable architecture leadership. - Proven experience in deploying and operating LLMs in production at scale, including evaluation, guardrails, and cost/performance optimization. - Strong knowledge of AWS services such as Bedrock, Lambda, EKS, and S3, cloud-native design principles, and distributed systems. - Experience with CI/CD, container orchestration using Kubernetes, and infrastructure automation with Terraform/CloudFormation. - Strong understanding of ML fundamentals and the ability to bridge research and engineering by considering model metrics, latency/throughput, inference constraints, and practical deployment. - Proficiency with SQL databases like PostgreSQL and scalable data access patterns. - Familiarity with JavaScript/TypeScript and full-stack integration patterns for AI products. - Experience in building or operating observability platforms like CloudWatch, Prometheus, Grafana, and defining SLO-based operations. Morningstar offers a hybrid work environment that allows you to collaborate in person each week, with most locations following a four days in-office model. As a Principal AI Engineer, you will play a crucial role in defining and driving the technical direction for the systems that power our AI products. Your responsibilities will include: - Architecting and scaling the AI platform and services to support the assessment of Environmental, Social, and Governance (ESG) performance of companies by investors worldwide. - Designing and scaling LLM and ML inference platforms, including routing, caching, model lifecycle, and API standards. - Establishing production patterns for RAG, retrieval systems, vector databases, and knowledge pipelines while setting best practices for quality and relevance. - Leading the development of platform-level capabilities such as orchestration frameworks, event-driven services, and reusable microservice components with a Python-first approach. - Defining and enforcing SLOs, reliability standards, observability practices, and incident response playbooks for AI services. - Driving cost governance for AI, including infrastructure scaling, caching, batching, and evaluation-driven deployment decisions. - Building robust CI/CD and release strategies for AI systems and automating deployments on AWS. - Providing technical mentorship and influencing design decisions across teams through design reviews and architecture forums. - Evaluating new technologies and guiding their adoption with a pragmatic lens. Qualifications required for this role include: - Expert-level programming skills in Python for developing services, APIs, pipelines, and platform components. - 9+ years of experience in AI engineering, MLOps, backend/platform engineering, or related roles with demonstrable architecture leadership. - Proven experience in deploying and operating LLMs in production at scale, including evaluation, guardrails, and cost/performance optimization. - Strong knowledge of AWS services such as Bedrock, Lambda, EKS, and S3, cloud-native design principles, and distributed systems. - Experience with CI/CD, container orchestration using Kubernetes, and infrastructure automation with Terraform/CloudFormation. - Strong understanding of ML fundamentals and the ability to bridge research and engineering by considering model metrics, latency/throughput, inference