Job Description
Description
A Lead AI/ML & MLOps Engineer to join our Canadian team. This is a senior, dual-purpose role:
Delivery leadership: leading the technical execution of AI and ML engagements for our clients, from data foundations through model deployment and operation.
Pre-sales and pipeline partnership: working alongside our sales organisation to shape, scope, and win new opportunities, with a specific focus on supporting deals that move through our partners motion.
You will be the senior technical voice in the room when we design AI/ML engagements: validating architectures, choosing tooling, scoping work, and standing behind the engineers who build it. You will also be a credible counterpart to client CTOs, data leaders, and partner technical sellers.
Key Responsibilities
Delivery and technical leadership
Lead the architecture and hands-on implementation of end-to-end ML systems: data ingestion, pipelines, feature stores, training, evaluation, serving, and monitoring.
Own technical decisions across the full stack, data platform, training environment, model serving, and MLOps tooling.
Set engineering standards for ML projects: experiment tracking, model versioning, reproducibility, governance, observability, drift monitoring, and CI/CD for ML.
Coach and uplift other engineers on the team in modern ML and MLOps practices.
Stay accountable for quality, security, and operational soundness of what we ship.
Pre-Sales and pipeline support
Partner with the sales leadership team across pre-sales activity: discovery calls, scoping workshops, technical briefings, and LOE preparation.
Lead architecture and solutioning conversations with prospects and customers, translate business problems into credible, defensible technical approaches.
Provide dedicated technical support to opportunities flowing through the partners sales process, including positioning their products as part of broader data and AI architectures, joint solutioning sessions, and partner-aligned proposals.
Contribute to thought leadership and demand generation: blog posts, webinars, capability decks, conference talks, and reference architectures.
Requirements
Required Experience and Skills
Machine Learning fundamentals
Strong grounding in the full ML lifecycle: data pipeline creation, feature engineering, model training, evaluation, deployment, and monitoring.
Production experience designing and building data pipelines that feed ML workloads (batch and streaming).
Solid hands-on understanding of model training: hyperparameter tuning, validation strategies, dealing with class imbalance, leakage, common failure modes.
Ability to select appropriate model families (classical ML, deep learning, large language models) for the problem at hand and justify the choice.
Hands-on production experience with the core MLOps building blocks:
Model registry and model versioning
Experiment tracking and reproducibility
Training pipelines and orchestration
CI/CD for ML (model and data)
Model serving (online, batch, streaming)
Model observability, performance, drift, data quality, and operational metrics
Governance, lineage, and access control
Experience with at least one major MLOps / experiment platform, for example MLflow, Weights & Biases, Vertex AI, SageMaker, Azure ML, or Databricks, is required. Cross-platform experience is preferred.
Cloud Platforms
Production experience building and operating ML systems on at least one major cloud: GCP, AWS, or Azure.
Strong comfort with the data and AI services on that cloud (e.g. BigQuery / Vertex AI, Redshift / SageMaker, Synapse / Azure ML).
Cross-cloud experience and the ability to make pragmatic platform recommendations is a strong plus.
Model Trust and Explainability
Practical experience with model explainability techniques: SHAP, LIME, feature attribution, partial dependence, model cards.
Familiarity with responsible AI practices: bias evaluation, fairness, calibration, uncertainty quantification, and confidence-aware UX patterns (e.g. withholding low-confidence predictions).
Awareness of what it takes to make a model trustworthy in regulated or high-stakes domains.
Agentic AI
Hands-on experience designing and shipping agentic AI solutions in production or production-adjacent settings.
Strong understanding of common agent design patterns, ReAct, plan-and-execute, tool use, reflection, multi-agent orchestration, human-in-the-loop.
Working experience with one or more agent frameworks (e.g. LangChain / LangGraph, LlamaIndex, CrewAI, etc.) and vector databases.
Sound judgement on when an agent is the right tool, and when a simpler approach is.
Data Platforms
Strong working knowledge of modern data platforms, relational, NoSQL, warehouse, and lakehouse.
MongoDB experience (Atlas, Atlas Vector Search, change streams, schema design for analytical and AI workloads) is highly valued.
Familiarity with BigQuery, Snowflake, and Databricks is a plus.
Ways of Working
Comfortable in a consulting setting: multiple concurrent engagements, ambiguity, scoping under time pressure, and frequent client interaction.
Strong written and verbal communication, able to hold a technical conversation with a CTO and explain a model decision to a non-technical or business stakeholder in the same hour.
Prior experience supporting pre-sales activity (scoping, technical proposals) is strongly preferred.
Comfortable being on camera and in the room with prospects and partners.
Nice to Have
Experience in regulated industries (healthcare, life sciences, financial services, public sector).
Production experience with RAG, vector search, and LLM evaluation frameworks.
Open-source contributions, public talks, or technical writing.
Prior experience working inside a cloud or data partner ecosystem (MongoDB, GCP, AWS, Azure, Databricks, Snowflake).
