Posted 5 days ago
Senior AI/ML Developer – Python | DSPy | LLM | Databricks | MLflow
@ ThoughtStorm
Toronto, Ontario
SIGNALS
Overview
ThoughtStorm is hiring a contract Senior AI/ML Developer to build and support LLM and machine learning workflows in Python. The work covers data processing, batch inference pipelines, model evaluation and experiment tracking, using DSPy, Databricks and MLflow.
Responsibilities:
- Build Python workflows for AI/ML development, data processing and automation
- Run and troubleshoot LLM batch inference jobs
- Evaluate and benchmark models, tracking metrics and analyzing errors
- Prepare, clean, annotate and manage datasets
Requirements summary:
- Strong hands-on Python experience for AI/ML
- Experience with Pandas, Scikit-learn and PyTorch
- Practical exposure to DSPy, LangChain or Hugging Face
- Experience with Databricks or cloud ML environments
- Familiarity with MLflow for experiment tracking
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-11-05
- First seen by our source
- 2026-11-10
- Source record updated
- 2026-11-10
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Software development |
| Travel | air: none · ground: none |
| Work setting | Office, Home |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
Summary and details written by AI from the posting; the employer’s posting is the authority.
ThoughtStorm
| Field | Value |
|---|---|
| Open jobs on Jobsy | 9 |
Job description
Required Skills & Qualifications
- Strong hands-on experience with Python programming for AI/ML development, data processing, and automation.
- Familiarity with Python libraries such as Pandas, Scikit-learn, and PyTorch.
- Practical exposure to LLM frameworks, particularly DSPy, LangChain, Hugging Face, or equivalent.
- Experience working with Databricks or other cloud-based Machine Learning environments.
- Familiarity with MLflow for experiment tracking, model management, and performance monitoring.
- Understanding of LLM inference pipelines, including executing batch inference jobs, troubleshooting, and monitoring model outputs.
- Experience supporting model evaluation, benchmarking, performance metrics tracking, and error analysis.
- Ability to prepare, clean, annotate, and manage datasets, maintain reproducible Python scripts, and work with Jupyter Notebooks.
Journal
Only you can see this. Kept on this device, and in your account when you sign in.