Machine learning, AI, QA

"We're Hiring! Multiple AI & Machine Learning Roles (Lead, Platform, Agentic, & QA)"

Location: Toronto, Canada.

1. Machine Learning Technical Lead

Role: Lead the delivery of production-grade AI solutions.

Key Requirements: 10+ years in ML/AI; mastery of Python, SQL, and PyTorch/TensorFlow. Expert in MLOps (MLflow, Airflow, Feature Stores) and deploying scalable REST/gRPC services.

Infrastructure: Deep experience with Kubernetes, Docker, and Cloud (Azure/AWS/GCP).

Preferred: Databricks/Spark experience, Responsible AI (explainability/fairness), and GenAI/RAG patterns.

Focus: Driving clarity in ambiguity and owning end-to-end model lifecycles.

2. Machine Learning Platform Engineer

Role: Build the backbone for seamless ML productionalization.

Key Requirements: Strong Software Engineering (Clean Architecture, API design) and Python/SQL skills. Hands-on experience with MLflow, experiment tracking, and model versioning.

Tech Stack: Scikit-learn, XGBoost, PyTorch; proficient in CI/CD and containerized deployments.

Preferred: Databricks (Unity Catalog/Workspaces), Spark for large-scale data, and model serving optimization (FastAPI, ONNX, quantization).

Focus: Balancing latency, cost, and reliability in ML services.

3. Agentic AI Developer

Role: Design autonomous AI agents and multi-step LLM workflows.

Key Requirements: Expertise in LangChain/LangGraph or LlamaIndex and vector databases (Pinecone/Chroma). Mastery of prompt engineering, tool/function calling, and RAG.

Software Skills: Production Python API design, SQL, and data engineering fundamentals.

Preferred: Serving open-source LLMs (vLLM, Ollama), advanced agent patterns (reflection/memory), and Databricks/Unity Catalog.

Focus: Building reliable, non-deterministic systems with robust evaluation harnesses.

4. QA/Validation Engineer (Agentic AI & ML)

Role: Ensure the quality and reliability of complex AI/ML systems.

Key Requirements: Experience in ML validation (leakage checks, metric selection) and GenAI testing (evaluating non-deterministic outputs). Strong Python and SQL automation skills.

Tools: MLflow, Docker, CI/CD, and data validation libraries (Pandas/NumPy).

Preferred: Great Expectations or Deequ for data quality; red-teaming LLMs; observability via OpenTelemetry.

Focus: Translating AI quality risks into automated, actionable engineering safeguards.

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