Lead AI/ML, MLOps Consultant

gravity9

Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.

Posted 5/18/2026full-timeRemote • 🇨🇦 CanadaSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftAWSAzureBigQueryCloudGoogle Cloud PlatformMongoDBNoSQLReact

About the role

Key responsibilities & impact

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

What you’ll need

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
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
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
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
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
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.

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account gravity9 Website LinkedIn All Job Openings 51 - 200 employees 🤖 Artificial Intelligence 🏢 Enterprise Artificial Intelligence
Enterprise gravity9 is a digital transformation company specialized in modernizing legacy systems by leveraging cutting-edge technologies such as AI, cloud computing, and microservices architecture. They offer solutions to propel organizations into the digital age, enhancing agility, scalability, and competitive advantage. gravity9's approach involves integrating innovative engineering practices to streamline operations, reduce technical debt, and future-proof digital infrastructures. They focus on strategic modernization that not only overcomes legacy constraints but also empowers businesses to thrive in today's dynamic market environment. Lead AI/ML, MLOps Consultant Job not on LinkedIn 🔥 7 minutes ago 🇨🇦 Canada – Remote ⏰ Full Time 🟠 Senior 🤖 Machine Learning Engineer Amazon Redshift AWS Azure BigQuery Cloud Google Cloud Platform MongoDB NoSQL React Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
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
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
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
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
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
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
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
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Hard Skills & Tools

Machine Learningdata pipeline creationfeature engineeringmodel trainingmodel evaluationmodel deploymentmodel monitoringhyperparameter tuningmodel versioningMLOps

Soft Skills

technical leadershipcoachingcommunicationconsultingproblem-solvingcollaborationaccountabilitythought leadershipclient interactionadaptability