RQ11580 – AI/ML Engineer (LLM, Python, Databricks) | Ontario Government | Onsite Toronto
- Toronto, ON, Canada
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Original job description
Please send your resume at anila.dada@amyantek.com if you are interested in this approx. 04 months contract with Ontario Public Service (Ministry of Public and Business Service Delivery and Procurement) with a possibility of extension, If you are not interested, please feel free to pass it in your network for anyone looking for work.
Job Title: RQ11580 - Senior Software Developer
Working Status: On-Site
Location: 222 Jarvis Street
Start Date: 2026-09-10
End Date: 2027-03-31
Hours per day: 7.25
Description
The Government of Ontario is embarking on a large Data & AI transformation initiative. Core to the transformation is the enablement of the Ontario Data Platform (ODP), which is being developed as the enterprise technology ecosystem for everything Data & AI. ODP is a modern ecosystem of data solution consisting of data ingestion technologies, storage, processing and visualization as well as solutions related to data governance, quality, marketplace, etc. ODP will initially serve all internal government clusters, ministries and program areas, but also external agencies in the future. We are looking for a data scientist to support various initiatives related to the platform as well as the execution of use cases to support the needs of various businesses within the government.
Must Have:
- Background in Computer Science, Data Science, or AI/ML (student or recent grad is good).
- Familiarity with Python and libraries like pandas, scikit-learn, or PyTorch.
- Exposure to LLM frameworks (DSPy, LangChain, Hugging Face, or equivalent).
- Comfortable working with Databricks / MLflow or other cloud ML environments.
- Detail-oriented with strong organizational skills for tracking experiments.
Key Responsibilities
- Run and Monitor Inferences
- Execute batch inference jobs on Databricks and other environments.
- Troubleshoot inference issues, log results, and escalate model anomalies.
- Pipeline & Evaluation Support
- Assist with running DSPy pipelines and loading custom models/configs.
- Support evaluation runs using internal metrics
- Track and summarize performance metrics for inference experiments.
- Data Handling & Experimentation
- Help prepare and clean legal text datasets for inference and evaluation.
- Annotate outputs to support error analysis and benchmarking.
- Maintain reproducible scripts/notebooks for inference runs.
Evaluation Criteria:
- Technical Skills - 50%
- LLM frameworks - 25%
- Databricks/MLflow - 25%
More about this job
Responsibilities
Run and monitor batch inference jobs, troubleshoot issues, record results, and escalate model anomalies across Databricks and other environments. Support DSPy pipelines and model evaluations, prepare legal text datasets, analyze outputs, track performance metrics, and maintain reproducible scripts and notebooks.
Requirements
The posting seeks a candidate with a background in computer science, data science, or AI/ML, and familiarity with Python and libraries such as pandas, scikit-learn, or PyTorch. Candidates should have exposure to LLM frameworks, experience or familiarity with Databricks, MLflow, or other cloud ML environments, and strong attention to detail and organizational skills.
Skills
- Python
- Pandas
- Scikit-Learn
- PyTorch
- Large Language Models
- DSPy
- LangChain
- Hugging Face
- Databricks
- MLflow
- Batch Inference
- Model Evaluation
- Data Cleaning
- Experiment Tracking
- Error Analysis
- Legal Text Data
Visa sponsorship
Not detected in the job text
Hiring contact email
anila.dada@amyantek.com
Categories
- Data & Analytics
- Technology
- Software
- Science & Research
- Government & Public Sector
Keywords
- Artificial Intelligence
- Machine Learning
- Data Science
- Computer Science
- Python
- Pandas
- Scikit-Learn
- PyTorch
- Large Language Models
- DSPy
- LangChain
- Hugging Face
- Databricks
- MLflow
- Cloud Machine Learning
- Batch Inference
- Inference Monitoring
- Model Evaluation
- Performance Metrics
- Data Cleaning
- Legal Text Datasets
- Error Analysis
- Benchmarking
- Experiment Tracking
- Reproducible Notebooks
- Data Ingestion
- Data Governance
- Data Quality
- Ontario Data Platform
- Ontario Public Service