Senior Software Developer - Toronto

We are seeking a dedicated and technically driven Senior Software Developer for a contract opportunity based in Toronto. In this role, you will take on an impactful AI/ML engineering, large language model (LLM) pipeline, and model inference capacity, specializing in executing batch inferences, evaluating model outputs, and managing reproducible data science workflows.
Operating fully onsite in Toronto, you will work closely with engineering teams to run and monitor inference jobs in Databricks and cloud machine learning environments, assist with advanced prompt and LLM pipelines using frameworks such as DSPy, LangChain, or Hugging Face, and track core performance metrics. This position demands a meticulous developer with practical foundation in Python, modern machine learning libraries, data preparation, and experiment tracking who can ensure reliable, high-quality model evaluations and streamlined ML operations.
Location: Toronto, ON
Assignment Type: Onsite (5 days per week)
Contract Duration: 7-month contract (with potential for extension)
Advantages
Modern LLM Frameworks: Hands‑on exposure to cutting‑edge AI and LLM orchestration tools, including DSPy, LangChain, and Hugging Face.
Cloud ML & Databricks: Direct practical experience running scalable batch inference workloads and tracking experiments in Databricks and MLflow.
Specialized Domain Data: Work with complex, domain‑specific text datasets, contributing directly to error analysis, benchmarking, and prompt evaluation.
Collaborative Environment: Engage in high‑impact machine learning operations with dedicated technical mentorship and growth opportunities.
Responsibilities
Execute and monitor batch inference jobs across Databricks and cloud‑based machine learning environments.
Troubleshoot inference execution issues, log experiment outcomes, and escalated model output anomalies.
Assist in configuring, testing, and running pipeline workflows using DSPy and related LLM frameworks.
Load, test, and validate custom model weights, tokenizers, and configuration files.
Support model evaluation runs using defined internal benchmarks and accuracy metrics.
Track, record, and summarize performance metrics across multiple inference experiments.
Prepare, preprocess, and clean specialized text datasets for inference and evaluation pipelines.
Annotate and review model outputs to facilitate error analysis, qualitative evaluations, and benchmarking.
Maintain clean, well‑documented, and reproducible notebooks and Python scripts for all experimental runs.
Collaborate with engineering peers to ensure data handling standards and model versioning best practices.
QualificationsCore Technical & AI/ML Requirements
Educational Background: Degree or background in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related discipline.
Programming & Libraries: Strong familiarity with Python and core data science libraries such as Pandas, Scikit‑learn, or PyTorch.
LLM Frameworks: Practical exposure to LLM frameworks and tools, such as DSPy, LangChain, Hugging Face Transformers, or equivalent toolsets.
ML Platforms & Tooling: Comfortable working with Databricks, MLflow, or equivalent cloud ML platforms for running notebooks and tracking experiments.
Data Preparation & Text Processing: Experience handling, cleaning, and preprocessing unstructured text datasets for model ingestion and benchmarking.
Preferred Technical Assets
Model Inference & Batch Jobs: Familiarity running inference scripts, handling API rate limits, or processing batch data workloads.
Experimentation Rigor: Experience with model evaluation methodologies, error classification, and metrics tracking.
Soft Skills & Delivery Attributes
Attention to Detail: High precision and strong organizational discipline for tracking experiments, hyperparameters, and evaluation outputs.
Analytical Problem Solving: Ability to diagnose data discrepancies, script errors, and inference failures efficiently.
Collaboration & Communication: Clear written and verbal communication skills to document findings and collaborate effectively within technical teams.
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity‑seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non‑binary/gender non‑conforming;
Indigenous or Aboriginal Peoples;
persons with disabilities (visible or invisible) and;
members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to View email address on ca.hrm.tc to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
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