Lead AI/ML & MLOps Engineer

About

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.

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).