Job Description

About the Role

As a Machine Learning Operations Engineer, you will be responsible for developing and maintaining the cutting edge systems that bring our AI products to life.


You will design, deploy, and scale the systems that power our AI products, enabling investors worldwide to assess the Environmental, Social, and Governance (ESG) performance of companies. Your focus will be on

production-grade ML infrastructure

: inference endpoints, orchestration, data pipelines, and scalable APIs.


We are looking for engineers who bring a

software development mindset

into MLOps -- testing, monitoring, documentation, and reliability -- while also understanding

machine learning principles and LLM

s in

production trade-offs

.

Responsibilities

Build and scale

inference endpoints

and APIs for both classic ML models and

LLMs

. Develop

CI/CD pipelines

and automate deployment on AWS (Bedrock, Lambda, EKS, S3, etc). Design and maintain

data pipelines

, queues, and event-driven workflows. Integrate

vector databases, MCP servers, and retrieval pipelines

into production systems. Contribute to

microservices

in Python and support our

orchestrator

layer. Ensure

monitoring, observability, and cost-aware operation

of deployed ML services. Collaborate with AI researchers and software engineers to productize prototypes.

Qualifications

Strong programming skills in

Python

(APIs, pipelines, services).

3+

years

experience in MLOps, backend engineering, data engineering or related roles. Good knowledge of

ML principles

(e.g. precision, recall, inference time, latency/throughput trade-offs). Solid knowledge of

AWS services

(Bedrock, Lambda, EKS, S3, etc). Experience with

CI/CD

pipelines

, containerization (Docker/Kubernetes). Understanding of

microservices

architectures, queues/events, and scalability

. Experience with

SQL databases

(PostgreSQL). Good communication skills and a

product-first mindset

.

Nice to Have

Hands-on experience

deploying and operating LLMs in production

, with awareness of

limitations, evaluation, and cost implications

. Experience with

JavaScript/TypeScript

Experience with Harness

Familiarity with

retrieval-augmented generation (RAG), vector

DBs

. Monitoring/observability tools (CloudWatch, Prometheus, Grafana). Infrastructure-as-code (Terraform, Cloudformation). Experience with

web crawlers

or large-scale data ingestion.
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.


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