Posted yesterday
Software Developer - Senior
@ Upstaff
Toronto, Ontario
Responsibilities:
- Execute batch inference jobs using Databricks and other environments.
- Monitor inference processes and troubleshoot execution issues.
- Maintain logs and report model anomalies.
- Escalate technical issues when required.
Requirements summary:
- Escalate technical issues when required.
- Required technical skills
- Python libraries
- pandas, scikit-learn, PyTorch
- LLM frameworks
- DSPy, LangChain, Hugging Face or equivalent
Source and dates
Source type not verified · zoho
- Employer posting date
- 2026-11-08
- First seen by our source
- 2026-11-09
- Source record updated
- 2026-11-09
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Upstaff
| Field | Value |
|---|---|
| Open jobs on Jobsy | 17 |
Project overview
The Government of Ontario is undertaking a major Data and AI transformation initiative focused on developing the Ontario Data Platform (ODP).
The ODP is an enterprise technology ecosystem designed to support data and AI solutions across government ministries, clusters, and program areas.
The platform includes:
Data ingestion technologies.
Data storage and processing.
Data visualization.
Data governance and quality.
Data marketplace solutions.
Machine learning and AI capabilities.
The selected candidate will support Data Science and AI initiatives, particularly the execution, monitoring, and evaluation of Large Language Model (LLM) inference experiments.
Key responsibilities
LLM Inference Execution and Monitoring
Execute batch inference jobs using Databricks and other environments.
Monitor inference processes and troubleshoot execution issues.
Maintain logs and report model anomalies.
Escalate technical issues when required.
Pipeline Development and Evaluation Support
Assist in running DSPy pipelines.
Load and configure custom models and configurations.
Support model evaluation using internal metrics.
Track, analyze, and summarize inference experiment performance.
Data Preparation and Experimentation
Prepare, clean, and organize legal text datasets for inference and evaluation.
Annotate model outputs to support error analysis and benchmarking.
Maintain reproducible scripts and notebooks for inference experiments.
Required technical skills
Skill area
Requirements
Programming
Python
Python libraries
pandas, scikit-learn, PyTorch
LLM frameworks
DSPy, LangChain, Hugging Face or equivalent
Data and ML platforms
Databricks, MLflow
Machine Learning
Model inference, evaluation and experimentation
Data handling
Dataset preparation, cleaning and annotation
Documentation
Reproducible scripts, notebooks and experiment tracking
Candidate qualifications
Educational background in Computer Science, Data Science, Artificial Intelligence or Machine Learning.
Students and recent graduates are welcome.
Familiarity with Python and machine learning libraries.
Exposure to LLM frameworks and model experimentation.
Experience or familiarity with Databricks, MLflow or other cloud-based ML environments.
Strong attention to detail and organizational skills.
Journal
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