Top AI Jobs in Ottawa

Design, build, deploy, and optimize production-grade AI systems, including LLM applications, autonomous agents, multi-agent workflows, machine-learning models, data and inference pipelines, and evaluation frameworks. Develop secure, scalable AI services using retrieval-augmented generation, tool calling, orchestration, and model fine-tuning. Monitor reliability, performance, safety, cost, and business outcomes while applying governance, observability, and responsible AI practices.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Design, build, and deploy production-grade AI applications, integrations, pipelines, and agentic workflows for customers. Lead discovery, prototyping, architecture workshops, technical troubleshooting, and customer go-lives. Integrate LLMs, retrieval systems, tools, and orchestration into enterprise environments, establish reusable engineering patterns, mentor customer-facing engineers, and translate customer feedback into product and engineering priorities.

Lead the strategy, architecture, deployment, and optimization of Salesforce Agentforce agents and AI orchestration systems. Build Data360 and Snowflake integrations supporting unified customer profiles, design prompts and agent logic, establish governance and safety controls, and oversee real-time data synchronization. Partner with Sales, Support, and Customer Success leaders to transform workflows and customer journeys into autonomous systems.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Design and scale AI-powered sales enablement experiences: architecture for generative AI, RAG, AI agents, content discovery, guided selling, coaching automation, governance, pilots, integrations with enablement/CRM/LMS systems, and measure adoption and business impact.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Build and deploy AI/LLM-powered features and RAG pipelines for internet-scale threat intelligence. Prototype, fine-tune, evaluate, and optimize models; integrate AI into platform services; implement CI/CD, regression testing, and security guardrails; collaborate across product, frontend, and data teams to deliver real-time, low-latency AI-driven analytics and automation.

Lead a distributed AI/ML engineering team to deliver production-grade AI systems for manufacturing. Define technical vision, prioritize high-impact use cases (yield, quality, maintenance, inspection), design scalable architectures, partner with site and enterprise teams, ensure responsible AI governance, and drive adoption and measurable operational improvements across plants.

The Applied AI Backend Sr. Software Developer will design, develop, and maintain SaaS products, mentor junior engineers, and ensure robust backend services using AI tools.

Own client operating rhythm for Creative Operations and Systems/Process Design clients: maintain stakeholder context, turn vague requests into clear actions, use AI-enabled workflows for prep and follow-up, produce client-ready summaries, maintain decision logs and trackers, and coordinate with internal teams to prevent scope drift and improve account knowledge reuse.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Lead design and implementation of LLM-powered product features, integrate AI into Vanta systems, instrument evaluations and monitoring, collaborate with platform and product teams, make pragmatic technical tradeoffs, mentor engineers, and drive AI product quality and adoption to help customers accelerate compliance and risk management.

The Principal Engineer will architect and own the Shop Builder platform, defining APIs, developing tools for developers and AI agents, and ensuring the platform's technical standards and documentation are met.

Lead a team to deliver tools and API solutions for the Shop Builder platform. Coordinate cross-team dependencies and maintain engineering standards while actively contributing code and driving technical decisions.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

The engineer will develop features for an AI workspace platform, deploy AI solutions in public sector environments, and interact with clients.

Lead client relationships for major entertainment brands, translate creative vision into briefs and social campaigns, use AI-first tools and agentic workflows to plan, coordinate, review, and improve work. Protect quality, surface risks and opportunities, and encode reusable workflows. Drive account growth through strategic judgment, clear communication, and operational excellence.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.