Principal Data Scientist, Agentic AI Technical Lead

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