Machine Learning Resident for Theragraph involved in developing solutions for health datasets. Collaborating in a cross - functional team under the mentorship of an Amii Scientist.
About the role
- ML Ops Engineer supporting the operational lifecycle of AI-powered products at Achievers. Leading initiatives within a high-performing team in a hybrid work environment from Toronto.
Responsibilities
- Lead high-impact initiatives that shape how millions of people experience work around the world
- Bring your unique perspective to complex and challenging projects - apply your expertise in data science, influence technical direction, and share your knowledge with fellow team members
- Join a close-knit, no-ego, high-performing team that solves meaningful problems and celebrates successes together
- Work alongside an experienced leadership team who is genuinely invested in your career growth
- Thrive in a fast-paced, high-growth environment where innovation is encouraged and your voice truly matters
- Deploy and operate ML models and LLMs using Vertex AI, Cloud Run, and GKE
- Automate packaging, versioning, and release of models, prompts, embeddings, and related artifacts
- Design scalable inference architectures (sync, async, agentic), including batching and GPU/TPU autoscaling
- Build and maintain ML and GenAI workflows using Vertex AI Pipelines, Cloud Composer (Airflow), or custom orchestration
- Implement CI/CD for ML code and GenAI artifacts (prompts, fine-tuned models, evaluation suites)
- Schedule retraining, re-embedding, and re-indexing to ensure model freshness
- Manage and version prompts, system instructions, RAG components, and agent workflows
- Operationalize fine-tuned or custom models using Vertex AI tuning capabilities
- Implement logging, lineage, and metadata using Vertex ML Metadata and Cloud Logging
- Partner with data scientists, GenAI engineers, product managers, and engineers to deliver production-ready ML systems
Requirements
- Experience in MLOps, ML platform engineering, or cloud-based AI infrastructure
- Strong hands-on experience with GCP, especially Vertex AI (ML & GenAI), BigQuery/BigQuery ML, Cloud Run or GKE, and Cloud Composer
- Strong Python skills with experience in testing, CI/CD, containerization, and infrastructure automation (Terraform)
- Experience with LLM workflows: embeddings, vector databases, prompt engineering, and evaluation
- Exposure to agentic workflows and frameworks such as MCP
- Familiarity with Vertex AI Model Garden, tuning, monitoring, and vector search technologies
- Exposure to LLM safety, moderation, or red-teaming workflows
- Strong communication and cross-functional collaboration skills
- Detail-oriented, reliability-focused mindset
- Comfortable working in fast-evolving environments
- Strong sense of ownership and accountability
Benefits
- Rewards for your impact through our Recognition and Rewards program
- Health Benefits and Life Insurance Coverage beginning on your first day
- Parental Leave Top-up
- Employer matched RRSP contributions
- Flexible Vacation to recharge, so you can bring your best
- Employee and Family Assistance Program offering mental health, legal, and financial counselling
- Supported professional development and career growth (Linkedin Learning, mentorship)
- Employee-Led Employee Resource Groups that celebrate our diversity
- Regular events designed to build connection, belonging, and well-being
- Hybrid flexibility, with time in our beautiful Liberty Village, Toronto office
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Report this job
Found something wrong with the page? Please let us know by submitting a report below.
Senior ML Engineer leading the scaling and innovation of machine learning initiatives at Wisedocs. Collaborating with other engineers to integrate the ML system into the platform for insurance tech.
Lead ML Engineer developing scalable data pipelines and ML systems for Newfold Digital. Collaborate with cross - functional teams using Python, SQL, and cloud ML platforms in an applied ML environment.
Senior Machine Learning Engineer designing and optimizing ML/AI systems for digital forensic tools. Collaborating with cross - functional teams to lead initiatives and drive innovation in digital investigations.
Senior MLOps Engineer focusing on applied MLOps for CreatorIQ, bridging data science and production - grade efficiency. Responsible for annotation workflows and cost - efficient model evaluation.
Senior AI/ML Engineer leading design and deployment of high - impact AI solutions at CloudWerx. Bridging predictive modeling with next - generation Agentic AI to solve client challenges.
Machine Learning Resident developing AI - assisted tools for geotechnical site characterization. Involves collaboration with scientists and engineers to develop predictive ML models.
A(
Machine Learning Resident at Hines Health Services focusing on AI - driven medical workforce solutions. Supporting the development of scalable, AI - powered recruitment platforms over a 6 - month residency.
Software Engineer, Machine Learning developing next - generation AI technologies for clinical trial platforms. Building scalable data pipelines, optimizing model performance, and collaborating with cross - functional teams in a hybrid setting.
Senior Machine Learning Engineer at BenchSci focusing on developing machine learning models for biomedical applications. Collaborating with a team to enhance scientific experiments through AI - driven solutions.