Principal Data Scientist, Agentic AI Technical Lead

Robots & Pencils

. Provide technical oversight of all agentic AI workstreams from problem framing and design through evaluation, deployment, and ongoing operation .

Posted 10/7/2026full-timeRemote • CanadaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in AI/ML, particularly in leading technical delivery and overseeing multi-team programs. Proficient in model evaluation, MLOps, and translating complex technical concepts for diverse audiences.

Highest-signal resume keywords

12+ Years Experience In Data Science And AI/MLHands-On Experience With LLM And Agentic SystemsExpertise In MLOps And AI InfrastructureStrong Proficiency In Python And Modern Data Science StackExceptional Stakeholder Management And Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model EvaluationExperimentation DesignApplied StatisticsPrompt EngineeringFunction/Tool CallingMulti-Agent OrchestrationRAG ArchitecturesVector DatabasesStreaming LLM ResponsesSoftware Engineering Fundamentals

Soft Skills

Stakeholder ManagementCommunication SkillsMentoringLeadership

Tools & Technologies

AWSAmazon BedrockSQL DatabasesNoSQL DatabasesWorkflow Orchestration ToolsEvent-Driven Architectures

Industry Keywords

AI GovernanceResponsible AICompliance ConsiderationsRegulated IndustriesLife SciencesHealthcareFinancial Services

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsNoSQLPythonSQL

About the role

Key responsibilities & impact

Provide technical oversight of all agentic AI workstreams from problem framing and design through evaluation, deployment, and ongoing operation
Define technical strategy and reference architecture for agentic systems, including multi-agent orchestration, tool and function calling, RAG patterns, vector databases, embeddings, and streaming responses
Manage technical delivery across streams, resolving blockers and coordinating dependencies
Set standards for model development, experimentation, and research-to-production processes
Establish evaluation frameworks for LLM and agent performance covering quality, safety, hallucination, cost, and latency
Guide scalable ML platforms, pipelines, and workflow orchestration for event-driven, asynchronous operations
Ensure reliability, security, scalability, observability, monitoring, and production debugging
Serve as technical face of the AI/ML program to senior client stakeholders
Co-define the AI roadmap with executive leadership
Translate complex technical concepts for executive, engineering, and business audiences
Align data science, engineering, product, and business teams around shared priorities and measurable outcomes
Lead and influence a large, multi-team delivery organization
Define and champion data science and AI engineering standards
Review and elevate work quality across teams
Mentor technical leads and senior practitioners
Own high-stakes, program-wide technical decisions
Drive the long-term technical vision for agentic AI practice

Requirements

What you’ll need

12+ years of experience in data science and AI/ML
Record of taking AI systems from research to production at enterprise scale
Proven track record leading technical delivery across multiple concurrent workstreams or teams
Experience leading or technically overseeing large multi-team programs, including managing technical risk, dependencies, and delivery quality
Exceptional stakeholder management and communication skills, including credibility with senior executives and client leadership
Hands-on experience with LLM and agentic systems, including prompt engineering, function/tool calling, multi-agent orchestration, RAG architectures, vector databases, embeddings, and streaming LLM responses
Deep expertise in model evaluation, experimentation design, and applied statistics, including evaluation approaches for generative and agentic systems
Strong proficiency in Python and the modern data science and ML stack
Expertise in MLOps and AI infrastructure, including model versioning, monitoring, deployment automation, and reproducibility
Strong software engineering fundamentals, including system design, API design, code quality, and unit testing practices
Experience with distributed systems, event-driven architectures, and workflow orchestration tools
In-depth experience with AWS, especially Amazon Bedrock; working knowledge of other cloud platforms
Familiarity with SQL and NoSQL databases, including scalable design patterns
Working knowledge of AI governance, responsible AI, and compliance considerations in production environments
Experience supporting AI programs in regulated industries such as life sciences, healthcare, or financial services
Experience in a consulting or client-facing technical leadership role
Background in fine-tuning, evaluation automation, or agent safety and guardrails

Benefits

Comp & perks

Production-ready AI shipped in 30 to 45 days
Opportunity to work on AI systems integrating into enterprise operations
Opportunity to work with experienced teams averaging fifteen-plus years of experience