Lead AI/ML and MLOps Consultant

About the role

gravity9 is a boutique IT consulting company headquartered in the UK with offices in the US, Canada, Poland, Ireland, and Colombia. Our team has deep experience in engineering, experience design and product management. We enjoy a challenge and pride ourselves on working with our clients on their most complex problems, finding elegant and flexible solutions that help them transform their businesses. We are looking for 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.

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