Lead the design, adoption, and scaling of Generative and Agentic AI ecosystems for strategic clients. This role blends advisory, hands-on solutioning, and practice-building leadership to ensure AI delivers tangible, industry-specific business outcomes.
Responsibilities
Architect and implement modular, scalable AI platforms that integrate Large Language Models (LLMs), Small Language Models (SLMs), and multi-agent orchestration.
Serve as the anchor architect for major client accounts and lead C-level discussions to define AI roadmaps.
Envision, design, and deliver verticalized AI use cases across various industries including Banking, Healthcare, Retail, and Manufacturing.
Lead end-to-end model lifecycle: data preparation, training, fine-tuning, deployment, monitoring, and continuous improvement.
Establish enterprise standards for MLOps/LLMOps pipelines and build agent lifecycle governance frameworks.
Build and maintain evaluation harnesses for multimodal foundation models, design and analyze benchmarks, and collaborate with researchers to improve training recipes and capability assessments.
Compensation
The US base salary range for this full-time position is between $180,000 - $450,000 annually.
Responsibilities
Build and maintain an evaluation harness for held-out loss, performance across data domains, and capabilities in text, code, math, and reasoning.
Design evaluations for early checkpoints and smaller models using log-likelihood scoring, few-shot prompts, and generated completions.
Assess benchmark coverage and saturation, quantify uncertainty in results, and work with data researchers to detect training-data contamination.
Version datasets, prompts, graders, and inference settings so results can be reproduced and compared across checkpoints and training runs.
Analyze experiments and work with researchers to identify useful recipe changes, select checkpoints, and prioritize capability improvements.
A senior, hands-on role joining the Global Public Sector team to shape technical approaches, define technology stacks, build prototypes fast, and mentor the next generation of technical leaders.
Responsibilities
Take on the hardest technical problems across programs, stepping in where projects are stuck or the path forward is unclear
Lead the technical side of early engagements: work with clients to scope problems, shape proofs of concept and keep the scope needed to prove value as small as possible
Build working prototypes quickly, from AI agents and evaluation pipelines to data and knowledge systems, and take the most promising ones to production
Define the technology and architecture our solutions are built on, keeping them cohesive, secure and reusable across clients
Mentor engineers across a young organisation through pairing, design and code reviews, and set the bar for engineering excellence
Lead the technical delivery of production-grade AI systems across enterprise digital, data, and analytics environments.
Responsibilities
Accountable for all AI engineering activities with in the DADAI delivery factory.
Lead the design, build, and operation of enterprise AI solutions on a Microsoft-first stack, including Azure OpenAI, Azure AI Foundry, Copilot Studio, Semantic Kernel, and Azure services.
Remain highly hands-on: review architecture and code, build proofs of concept, troubleshoot complex issues, and guide implementation teams through production hardening.
Define and enforce engineering standards for AI delivery, including evaluation, monitoring, release controls, security, privacy, and Responsible AI practices in regulated environments.
Work across architects, platform teams, security, and delivery stakeholders to ensure AI solutions are scalable, supportable, compliant, and aligned to business needs.
Requirements
BSc/BA in Computer Science, Software Engineering, Information Systems, or equivalent experience
Build automated test infrastructure and evaluation workflows for AI-driven industrial software
Compensation
Salary is commensurate with experience, and ranges between $ 100,000 CAD - $ 150,000 CAD, excluding bonus and benefits. In addition to base salary, this role includes eligibility for an annual discretionary bonus of 10%
Responsibilities
Build automated test infrastructure for a product developed across multiple sites
Design evaluation methods for agent behavior
Build the assessment workflow into the product
Integrate security checks into the workflow
Keep test expectations independent of implementation
Requirements
Degree in computer science, software engineering, or a related field
Nice to have: agent evaluation frameworks
Nice to have: AI observability tooling (Langfuse, LangSmith, Promptfoo)
Nice to have: safety-relevant or regulated software experience
Nice to have: background in safety-relevant or regulated software
Lead the design, development, and deployment of enterprise AI/ML solutions, focusing on applied AI patterns, responsible AI principles, and mission‑critical technology industries.
Responsibilities
Design, develop, and deploy production‑grade AI/ML solutions within enterprise or large‑scale environments.
Apply AI solution patterns such as agentic architectures, Retrieval‑Augmented Generation (RAG), orchestration frameworks, evaluation pipelines, and intelligent automation.
Implement modern AI system concepts including memory management, planning, tool orchestration, model evaluation, guardrails, and responsible AI principles.
Develop APIs and front‑end components using Angular, React, and TypeScript.
Collaborate with aviation, aerospace, inflight entertainment, and other mission‑critical technology industries.
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