Lead Artificial Intelligence Engineer

Lead AI Engineer

Job Description — Lead AI Engineer

About the Role

As a Lead AI Engineer, you will be responsible for building and scaling the production systems that bring our AI products to life.

You will lead the design and implementation of AI-powered services that enable investors worldwide to assess the Environmental, Social, and Governance (ESG) performance of companies. Your focus will be on production-grade AI engineering: model and LLM inference services, orchestration, retrieval pipelines, scalable APIs, and robust data workflows.

Responsibilities

• Lead the development and scaling of inference endpoints and APIs for classic ML models and LLMs.
• Own end-to-end delivery of AI services from prototype to production: design, build, deploy, and operate.
• Develop and maintain CI/CD pipelines; automate deployments on AWS (e.g., Bedrock, Lambda, EKS, S3, etc.).
• Design and maintain data pipelines, queues, and event-driven workflows to support AI products.
• Integrate vector databases, retrieval pipelines, and RAG patterns into production-grade systems.
• Build and maintain orchestration layers and microservices in Python; contribute to platform services.
• Establish and maintain monitoring, observability, alerting, and cost-aware operations for AI services.
• Implement strong engineering practices: testing strategies, documentation, runbooks, and reliability standards.
• Collaborate with AI researchers and product engineering teams to productize prototypes and deliver measurable outcomes.
• Mentor and guide engineers; drive code quality, design reviews, and operational excellence.

Qualifications

• Strong programming skills in Python (APIs, services, pipelines).
• 7+ years of experience in AI engineering, MLOps, backend engineering, or related roles, with significant production ownership.
• Hands-on experience deploying and operating LLMs in production, with awareness of evaluation, limitations, and cost implications.
• Solid knowledge of AWS services (Bedrock, Lambda, EKS, S3, etc.).
• Experience with CI/CD, containerization (Docker/Kubernetes), and infrastructure automation.
• Understanding of microservices architectures, queues/events, and scalable system design.
• Working knowledge of ML concepts (precision/recall, latency/throughput trade-offs, inference performance).
• Experience with SQL databases (e.g., PostgreSQL).
• Strong communication skills, a product-first mindset, and the ability to translate ambiguity into execution.

Nice to Have

• Experience with JavaScript/TypeScript.
• Experience with Harness.
• Familiarity with monitoring/observability tools (CloudWatch, Prometheus, Grafana).
• Infrastructure-as-code experience (Terraform, CloudFormation).
• Experience with web crawlers or large-scale data ingestion.

Equal Opportunity & Work Environment

Morningstar is an equal opportunity employer.

Morningstar’s hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we’re at our best when we’re purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.

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Experience Level

Senior Level