Principal Data Scientist, Agentic AI Technical Lead

<h3>Accountabilities</h3><li>Provide technical oversight across all agentic AI workstreams, guiding initiatives from problem definition and architecture through evaluation, deployment, and ongoing operation.</li><li>Define the technical strategy and reference architecture for agentic AI systems, including multi-agent orchestration, tool and function calling, RAG, vector databases, embeddings, and streaming LLM responses.</li><li>Coordinate technical delivery across multiple teams and workstreams, identifying dependencies, resolving blockers, managing technical risk, and maintaining quality and velocity.</li><li>Establish standards for model development, experimentation, evaluation, and the transition from research concepts to reliable production systems.</li><li>Design and champion rigorous evaluation frameworks covering model and agent quality, safety, hallucination, cost, latency, and other meaningful performance indicators.</li><li>Guide the development of scalable ML platforms, pipelines, event-driven architectures, and workflow orchestration systems supporting asynchronous AI operations.</li><li>Ensure deployed AI systems meet high standards for reliability, security, scalability, observability, monitoring, and production debugging.</li><li>Serve as the senior technical representative for the AI/ML program with executive and client stakeholders, communicating progress, risks, trade-offs, and technical decisions clearly.</li><li>Partner with executive leadership to define and evolve the AI roadmap, contributing as a strategic technical peer on high-impact decisions.</li><li>Translate complex AI and data science concepts into actionable decisions for executives, engineers, product teams, and business stakeholders.</li><li>Align data science, engineering, product, and business teams around shared priorities, technical standards, and measurable outcomes.</li><li>Lead and influence a large, multi-team delivery organization while establishing expectations for technical excellence across workstreams.</li><li>Review technical work across teams, provide direct and constructive feedback, and raise the quality of architecture, implementation, and delivery.</li><li>Mentor technical leads and senior practitioners, helping develop the next generation of AI and technical leaders.</li><li>Own high-stakes technical decisions that affect the broader program, balancing innovation, delivery speed, reliability, and risk.</li><li>Continuously evolve the technical vision and define how agentic AI engineering and data science practices should mature across the program.</li><li>Use modern AI-assisted development tools and workflows to improve productivity, engineering quality, and delivery speed.</li><h2>Requirements</h2><li>12+ years of professional experience in data science and AI/ML, with a proven record of taking AI systems from research or experimentation into production at enterprise scale.</li><li>Demonstrated experience leading technical delivery across multiple concurrent workstreams, teams, or large AI programs, with influence extending beyond a single engineering team.</li><li>Proven ability to manage technical dependencies, risks, architectural decisions, and delivery quality across complex multi-team initiatives.</li><li>Exceptional stakeholder management and communication skills, including credibility with executives, client leadership, engineering teams, and business stakeholders.</li><li>Deep hands-on experience with LLM and agentic AI systems, including prompt engineering, tool and function calling, multi-agent orchestration, RAG architectures, vector databases, embeddings, and streaming LLM responses.</li><li>Strong expertise in model evaluation, experimentation design, applied statistics, and evaluation methodologies specifically suited to generative and agentic AI systems.</li><li>Advanced Python skills and strong proficiency across modern data science and machine learning technologies.</li><li>Extensive MLOps and AI infrastructure experience, including model versioning, monitoring, deployment automation, reproducibility, and production operations.</li><li>Strong software engineering fundamentals, including system design, API design, code quality, testing, and maintainable architecture.</li><li>Experience with distributed systems, event-driven architectures, and workflow orchestration technologies.</li><li>In-depth AWS experience, particularly with AWS GenAI services such as Amazon Bedrock, alongside working knowledge of other cloud platforms.</li><li>Familiarity with SQL and NoSQL databases and scalable data-storage and access patterns.</li><li>Working knowledge of AI governance, responsible AI, safety, and compliance considerations for production systems.</li><li>Strong ability to operate in ambiguous environments, make decisions with incomplete information, and balance technical excellence with practical delivery constraints.</li><li>Experience in consulting or client-facing technical leadership is advantageous.</li><li>Experience supporting AI programs in regulated industries such as healthcare, life sciences, or financial services is a plus.</li><li>Additional experience in fine-tuning, automated evaluation, agent safety, and AI guardrails is beneficial.</li><li>Willingness and ability to travel approximately 20% to the United States, as well as attend required in-person interviews and onboarding activities.</li><h2>Benefits</h2><li>Competitive remote opportunity based in Canada.</li><li>Opportunity to serve as the senior technical authority across a large, multi-workstream agentic AI program.</li><li>Significant influence over AI architecture, engineering standards, evaluation practices, and long-term technical strategy.</li><li>Exposure to advanced technologies across agentic AI, LLMs, RAG, AWS, MLOps, distributed systems, and AI governance.</li><li>Opportunity to collaborate directly with executive and senior client stakeholders on strategic AI initiatives.</li><li>Leadership and mentorship opportunities across a large technical delivery organization.</li><li>Fast-paced environment focused on delivering production-ready AI systems in approximately 30–45 days.</li><li>Work alongside experienced technical professionals and teams focused on high-quality engineering and practical AI innovation.</li><li>Opportunity to solve complex, high-impact problems across enterprise environments and help shape the future of agentic AI delivery.</li>