Top AI Jobs in Ottawa

Hunt and develop new AI consulting opportunities across North America. Source, qualify, and manage pipeline, conduct outreach and discovery, partner with leadership to close deals, and travel for client meetings (approx. 40%).

Hunter-oriented BDM responsible for sourcing and qualifying new enterprise and mid-market opportunities across North America, building pipelines, outbound prospecting, coordinating discovery, partnering with leadership to close deals, and attending events (travel ~40%). Focus on selling consulting and technology services.

Lead enterprise product vision and multi-year strategy for integrated product strategy and AI across ABC's portfolio. Build and manage a high-performing product and UX team, define AI-enabled platform and API-first capabilities, partner cross-functionally on go-to-market and technical decisions, and drive platform convergence, monetization, and measurable business outcomes.

Build and scale production AI systems, APIs, data pipelines, retrieval systems, and model-serving infrastructure for consumer and clinician-facing health products. Integrate and fine-tune LLMs and other models, manage AI feature lifecycles from prototyping through monitoring, and ensure reliability and observability. Collaborate with engineering, product, and science teams. More senior candidates may lead AI architecture, roadmap development, and mentoring.

Own the AI and data product roadmap, prioritize and sequence development with engineering and founders, ship trustworthy AI experiences, drive adoption, define feature success metrics, evaluate outcomes, and manage the analytics foundation. The role requires technical fluency in AI, prior software engineering experience, production AI product delivery, strong product judgment, and comfort working in a fast-paced, remote startup environment.

Drive technical pre-sales for Microsoft cloud database and analytics solutions via demos, PoCs, hackathons, and architecture workshops. Design secure, scalable data architectures, resolve technical blockers, collaborate with engineering, and evangelize Microsoft Fabric, Azure Databases, Databricks, Purview and EDW migrations to accelerate customer adoption.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

Audits AI-generated support responses for accuracy, completeness, hallucinations, compliance, and customer trust. Governs knowledge sources, identifies content and workflow gaps, analyzes AI failures, builds performance dashboards, and partners with Content, Product, Engineering, and Support teams to improve AI training, guardrails, workflows, and resolution quality.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Lead engineering execution and manage a senior team developing Thunderbolt, an open-source enterprise AI product. Establish scalable planning, code review, testing, release, documentation, and quality practices while contributing code and reviewing technical work. Partner with product and technical leadership to translate AI strategy into delivery plans, improve reliability and enterprise readiness, support customer-controlled architectures, and grow the engineering organization through hiring, coaching, and resource planning.

Own the generation side of Document Intelligence by designing prompts, schemas, validators, multimodal context assembly, and model-selection strategies. Build evaluation datasets and rubrics, monitor factuality and quality, identify regressions, and ship reliable LLM features into production. Work with video, PDF, audio, and image inputs to generate validated maintenance entities while balancing quality, cost, and latency.

Own the product vision, roadmap, and delivery for AI-enabled platforms across Aprio. Translate practice requirements into user stories, lead development lifecycles, coordinate onshore/offshore teams, validate and integrate LLMs and AI tooling, and engage stakeholders while ensuring responsible AI, data privacy, and client confidentiality.

Develop backend systems for 3D AutoReview, including geometry parsing, model analysis, quality detection, rule systems, scalable APIs, and AI-assisted review workflows. Collaborate with backend, frontend, product, and machine learning teams to deliver production features. Build extensible systems for custom 3D rules and reference data while experimenting with CAD, machine learning, and large language model technologies.

Lead backend development for AI-enabled SaaS: design scalable microservices, use AI coding assistants, own features end-to-end, enforce code quality/security, mentor engineers, and collaborate with product and SRE teams.

Build and deploy LLM agents, Python automations, data workflows, dashboards, and root-cause analysis tools. Analyze telecommunications data with SQL to improve network quality, reduce fraud and billing errors, and optimize margins. Establish data architecture, governance, KPIs, and safe AI orchestration frameworks. Partner cross-functionally with Carrier Relations, Technology, Product, Supply Operations, and Finance to deliver scalable data products and communicate actionable insights.

The AI Solutions Manager leads client engagements focused on AI and automation, managing project lifecycles, optimizing internal processes, and integrating AI tools. They also provide training and performance monitoring.

The Solution Architect designs AI solutions that automate workflows, collaborates with stakeholders, and integrates systems, ensuring continuous improvement and deployment.

Develop and train large-scale multimodal foundation models (vision and language) that fuse imaging with molecular data, build scalable distributed ML pipelines for petabyte-scale datasets, and collaborate with experimental scientists and engineers to productionize models and systems.

Lead development of AI-native production systems: convert AI-assisted prototypes into scalable, secure software; own architecture, reliability, and technical direction; direct and control AI coding agents; define engineering standards and workflows; collaborate with founders on product and strategy; debug complex issues and elevate engineering quality.

Create high-volume, high-quality marketing content using generative AI and AI agents (video, image, social). Develop timely campaign ideas, track AI and cultural trends, experiment with formats, and work closely with founders and the marketing team to shape brand voice and growth.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.