Machine Learning Operation Engineer

This job is with Morningstar, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

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       (Postgre

SQL).

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/Type Script

Experience with Harness

Familiarity with       retrieval-augmented generation (RAG), vector     DBs    .

Monitoring/observability tools (Cloud Watch, Prometheus, Grafana).

Infrastructure-as-code (Terraform,       Cloud formation  ).

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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