Morningstar unites problem solvers with a clear goal: helping investors achieve their financial objectives. As a leading investment research and data company, we stand out by how we apply our insights to serve a broad range of users. Our independent investment research, powered by cutting-edge technology and design, provides tailored solutions that meet users' needs.

With a strong foundation in data and innovation, we deliver comprehensive services to investors worldwide, empowering better decisions for individuals and those managing money for millions.

We are seeking a Senior Software Engineer to build the applied systems that bring AI capabilities into production: the data pipelines, LLM integrations, agentic workflows, tool interfaces, and evaluation frameworks that make AI-driven products reliable enough to depend on. success is measured in shipped, maintainable systems. You may be a strong fit if you love working within a landscape that changes quickly, creating durable architectures with swappable parts, so current models and techniques are adopted on evidence. The role encompasses fluency across cloud architecture and local model inference, evaluation design, agentic workflows and orchestration, API and tool design, and AI-assisted data enrichment.

It also requires the engineering rigor to establish reliable sources of truth, detect regressions, and recognize when a deterministic solution is more appropriate than an AI-driven one.

In most of our locations, our hybrid work model is four days in-office each week. This position is based in our Toronto office. We follow a hybrid policy of at least 4 days onsite.

Evaluate open-weight models against hosted options for cost and performance tradeoffs

Build and maintain an eval harness: curate golden questions, catch regressions before release, and treat eval results as the gate for shipping

Build and extend the pipeline that validates, resolves, and versions governed entities into a serving layer





Design and operate LLM-driven extraction and classification agents that propose structured data for human review rather than auto-publishing unreviewed AI output

Own structured content modeling against a headless CMS, including schema and versioning decisions that other teams depend on

Instrument pipelines and served surfaces for freshness, adoption, and answer-quality telemetry

Participate in and help run the weekly eval review and the biweekly skill-library session, harvesting reusable agent tooling for the team

Mentor engineers being reskilled into applied AI work, particularly around eval design and agentic-coding practices

5+ years of software engineering experience, including production API and data-pipeline design

Comfort working with agentic coding tools daily as a core part of the workflow

Strong proficiency in Python across eval tooling, service-level code, and API development (FastAPI or similar), plus working proficiency in TypeScript/Node.js for application integrations

Experience deploying and operating production services in AWS (or equivalent), including containerized workloads and infrastructure-as-code

Security and privacy judgment in AI systems: handling sensitive data appropriately, and designing against failure modes like prompt injection, data leakage through prompts, and unsafe or unattributed model output

Solid understanding of data pipeline patterns: idempotency, versioning, and staged architectures

Explicitly not required: formal model training or ML research credentials (e.g., Creative problem solver comfortable operating in ambiguity,



with a builder's bias toward shipping over ceremony

Experience evaluating or benchmarking open-weight models for cost/performance (inference-time evaluation, not training)

Amazon Bedrock, Azure AI/Cognitive Services) and centralized LLM gateways (e.g., Experience with headless CMS platforms and structured content modeling

Total Cash Compensation (Base Plus Bonus Target) 101,800.00 - 207,000.00 CAD

If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentiality to our Compliance team (days vary by region). If any conflicts of interest are identified, then you will be required to liquidate those holdings immediately. In addition, dependent on your department and location of work certain employee accounts must be held with an approved broker (for example all, U.S. employee accounts).

If this applies and your account(s) are not with an approved broker, you will be required to move your holdings to an approved broker.

In most of our locations, our hybrid work model is four days in-office each week. Morningstar is a global independent investment research and financial data company. Here, you'll help uncover what's hidden, simplify what's complex, and create insights that empower investor success.

Company overview Morningstar Development Program Morningstar is strongly committed to creating and preserving equal opportunity for all employees and applicants. We make all employment decisions - recruitment, hiring, compensation, training, promotion, transfer, discipline, termination, and other personnel matters - without regard to race, color, ancestry, religion, sex, national origin, age, disability, protected veteran status, marital status, sexual orientation, genetic information, citizenship, gender identity and expression, parental status, or other legally protected characteristics or conduct. #

📌 Senior Software Engineer- Applied AI (Toronto)
🏢 Morningstar Credit Ratings
📍 Toronto