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Added 14 days ago
Actuarial Analyst III - GI Reinsurance
TD
Toronto, ON · Montreal, QC
Full-time
Strong computer and programming skills (Excel, VBA, R, Python)
Added 15 days ago
Senior Software Engineer (Conductor)
PocketHealth
Toronto, ON
Full-time
…That means working across the full stack: backend services in Python and TypeScript, integrations against real healthcare provider environments (HL7 v2, FHIR, DICOM, RIS/PACS, EMR webhooks,…
Added 11 days ago
Applied AI Engineer
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Full-time
Strong Python, and comfort across the pipeline from ingestion to retrieval.
Added 15 days ago
Senior Controller Integration Engineering
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Added 1 month ago
AI Lead Architect, Software and Platforms Architecture (T & I) (Telework/Hybrid)
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Full-time
Added 1 month ago
AI Lead Architect, Software and Platforms Architecture (T & I) (Telework/Hybrid)
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RISK MFI Management Information Analyst
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A general all-around IT competence; python programming experience is an advantage.
Added 11 days ago
Specialist Software Cloud Developer
CPKC
Calgary, AB
Full-time
…requires an experienced hands-on full-stack cloud and data developer who can lead technical initiatives in collaboration with other developers, solution architects, business analysts, and…
Added 12 days ago
Senior Data Engineer - Zonda - Toronto, ON
CoStar
Toronto, ON
$120,000 to $150,000 CAD / year
Full-time
Develop and optimize data transformations and orchestrations using SQL, Python, dbt, Airflow, Glue, PostgreSQL, and Snowflake.
Added 11 days ago
Cloud & AI Apps Solution Engineer
Microsoft
Calgary, AB
Full-time
…containerization, microservices, event-driven architectures, Python, Java, and…
Added 12 days ago
Senior Data Engineer - Zonda - Toronto, ON
CoStar
Toronto, ON
$120,000 to $150,000 CAD / year
Full-time
Develop and optimize data transformations and orchestrations using SQL, Python, dbt, Airflow, Glue, PostgreSQL, and Snowflake.
Added 12 days ago
Senior Data Platform Engineer - Zonda - Toronto, ON
CoStar
Toronto, ON
$140,000 to $170,000 CAD / year
Full-time
Added 12 days ago
Technology and System Management Student
Ericsson
Ottawa, ON
Full-time
Programming experience in Python and/or C/C++
Added 12 days ago
FY27 Intern - MSIP Digital Design Engineering Internship - Canada (16 months)
Qualcomm
Toronto, ON
Full-time
Knowledge of Verilog/System Verilog, Python, Perl, C/C++, Linux/Unix environment
Added 12 days ago
FY27 Intern - MSIP Digital Design Verification Engineering Internship - Canada (16 months)
Qualcomm
Toronto, ON
Full-time
Tools/Languages: Python, Perl, C/C++, SystemVerilog, UVM, Linux/Unix environment.
Added 12 days ago
FY27 Intern - Silicon Validation Engineer, Electrical and Optical Systems Internship - Canada (16 months)
Qualcomm
Ottawa, ON
Full-time
Familiarity with Python and object-oriented programming concepts; experience developing or maintaining software tools is a strong asset
Added 11 days ago
Cloud, Automation & AI Engineer - Senior Consultant
Deloitte
Toronto, ON
Full-time
Experience developing automation solutions using scripting or programming languages such as Python, PowerShell, Bash, or similar.
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Project Estimation Manager [On-Site, Hybrid or Remote] - Gestionnaire Estimation des Projets [Sur Place, Hybride ou Télétravail] | - FR
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Montreal, QC
Full-time
Added 29 days ago
Data Engineer (Intermediate - Senior)
KF Aerospace
Kelowna, BC
$73,000 to $114,000 CAD / year
Full-time
Experience with Azure DevOps, REST APIs, Python, Spark, or Azure Data Factory is considered an asset.
Added 12 days ago
Associate Cloud Engineer
Aptiv
Ottawa, ON
Full-time
Design, develop, and maintain software modules using C, C++, and Python.
Added 12 days ago
Associate Cloud Engineer
Aptiv
Ottawa, ON · ON
Full-time
Design, develop, and maintain software modules using C, C++, and Python.
Added 11 days ago
Power System Engineering Intern-Calgary
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Toronto, ON
Full-time
Scripting/Programming (Python, C++, or other) proficiency and basic knowledge of power system software like ETAP, PSSE.
Added 11 days ago
Senior Software Engineer- Applied AI
Morningstar
Toronto, ON
Full-time
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.
Added 12 days ago
Associate Director DevOps Developer
RBC
Bedford, QC
Full-time
InnerSource and Developer Experience programs
Added 12 days ago
Project Manager - Digital Transformation
Sunrise Farms
Surrey, BC
Full-time
Hands-on technical experience such as in ELT, SQL Stored Procedures, Python or in pipes, coding, or scripting that connects to databases.

Morningstar

Senior Software Engineer- Applied AI

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 18, 2026
Experience: 5+ years of software engineering experience, including production API and data-pipeline design.
Posting language: English
Posted: Sep 18, 2026

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Original job description

Job Description

About Morningstar

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.

 

The Role

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. This is applied engineering rather than research; 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 new 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.

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.

This position is based in our Toronto office. We follow a hybrid policy of at least 4 days onsite.

 

Job Responsibilities

Integrate with hosted LLM inference (via centralized model gateway infrastructure) for extraction, classification, and agent orchestration workloads; evaluate open-weight models against hosted options for cost and performance tradeoffs.
Design and maintain adapters that sync source systems (CMS, event platforms, editorial, research libraries) into a governed dataset without duplicating source-of-record logic.
Build and maintain an eval harness: curate golden questions, catch regressions before release, and treat eval results as the gate for shipping.
Design MCP tools and API/GraphQL interfaces that separate "fuzzy" retrieval (ranked candidates, confidence scores) from "exact" governed lookups (deterministic, provenance-carrying).
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.
Contribute governance and vocabulary decisions upstream to org-wide standards where relevant, rather than duplicating them.

 

Qualifications

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.
Hands-on production experience integrating LLMs: Skills, MCP, RAG, structured outputs, and tool/function calling.
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.
Experience operating production systems against latency, reliability, and cost targets, including token-cost management for inference workloads.
Evaluation literacy: you can describe an eval you built, what it caught, and how you handled canonical truth, variance, and regression cases.
Experience with REST/GraphQL API design.
Solid understanding of data pipeline patterns: idempotency, versioning, and staged architectures.
Explicitly not required: formal model training or ML research credentials (e.g., pretraining, fine-tuning research). This is an applied systems role; we're looking for builders, not researchers.
Strong written communication. You can write a clear design doc, explain a tradeoff to a non-engineer, and document decisions others will build against.
Creative problem solver comfortable operating in ambiguity, with a builder's bias toward shipping over ceremony.

 

Nice to have

Experience evaluating or benchmarking open-weight models for cost/performance (inference-time evaluation, not training).
Experience with cloud-hosted inference services (e.g., Amazon Bedrock, Azure AI/Cognitive Services) and centralized LLM gateways (e.g., LiteLLM).
Experience with headless CMS platforms and structured content modeling.
Experience in a regulated or compliance-sensitive domain where provenance and auditability matter.

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). These investments will be reviewed to ensure they meet Code of Ethics requirements. 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.

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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More about this job

Responsibilities

The role involves building and maintaining applied AI systems, including data pipelines, LLM integrations, and agentic workflows for production environments. You will also design evaluation harnesses and telemetry systems to ensure the reliability and performance of AI-driven products.

Requirements

Candidates must have 5+ years of software engineering experience with strong proficiency in Python and production-level API and data pipeline design. Hands-on experience with LLM integration, cloud infrastructure (AWS), and a solid understanding of data governance and evaluation methodologies are required.

Skills

Python
TypeScript
Node.js
LLM integration
RAG
FastAPI
AWS
Data pipelines
API design
GraphQL
REST
Cloud architecture
Agentic workflows
Evaluation design
Infrastructure-as-code
Containerization

Benefits

Hybrid work environment
Flexibility

Visa sponsorship

Not detected in the job text

Keywords

Applied AI
LLM
Python
TypeScript
Node.js
FastAPI
AWS
GraphQL
REST
Data Pipelines
RAG
MCP
Infrastructure-as-code
Containerization
Telemetry
Governance
Agentic Workflows
Evaluation Harness
Headless CMS
Cloud Architecture
Software Engineering
Investment Research
Financial Services
Prompt Engineering
Idempotency

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