Director, AI Forward Deployed Researcher

Forward Deployed Researcher, Director TELUS Digital is a leading digital customer experience (CX) innovator that designs, builds, and delivers next-generation solutions, including the Fuel iX generative AI platform - an enterprise-scale platform proven with over 50,000 users that enables organisations to safely manage their AI applications, foundation models, and data sources. Role Purpose The Forward Deployed Researcher, Director is a strategic leader, advanced AI research specialist, and manager who directly oversees and engages in the highest-value customer implementations. This role is responsible for driving frontier AI research into production, setting the strategic technical direction for enterprise clients, and managing a team of Forward Deployed Researchers and Engineers. This role operates at the critical intersection of cutting-edge research, software engineering, executive-level strategic consulting, and team management. As a Forward Deployed Researcher, Director, you will embed directly within Fortune 500 and highly regulated customer organizations to advance frontier AI implementations, shape AI strategy, deliver bespoke production systems, and build world-class technical teams. Unlike traditional research leaders who remain in labs or remote environments, you will be embedded within customer teams - walking the halls of banks, healthcare organizations, and tech companies - to understand their unique challenges, conduct applied research, and lead the delivery of transformative AI solutions grounded in both frontier techniques and practical enterprise constraints. Key Responsibilities 1. Strategic AI Research create career development pathways and technical competency frameworks Serve as an internal thought leader, sharing research insights, lessons learned, and best practices across the Forward Deployed Engineer community Mentor junior Forward Deployed Engineers and Solutions Engineers on technical, consulting, and leadership skills Foster a culture of innovation, customer-centricity, continuous improvement, and technical excellence within the team Establish metrics, KPIs, and performance management systems for team success and customer impact 3. Customer-Embedded Research maintain hands-on expertise in cutting-edge areas Experiment with emerging AI technologies and assess applicability to customer challenges and competitive positioning Publish or present technical work, contributing to TELUS Digital's thought leadership and industry recognition Build and maintain expertise in frontier areas (LLMs, multimodal AI, agentic systems, synthetic data, RLHF, evaluation frameworks, etc.) Document technical approaches, architectures, research methodologies, and lessons learned Contribute to open-source projects or TELUS Digital's technical community Qualifications Required 10+ years of hands-on software engineering and AI/ML experience with production systems 4+ years in senior leadership or management roles managing technical teams of at least 5+ professionals (e.g., managing Forward Deployed Engineers, Solution Engineers, or Research teams) Proven ability to serve as a strategic advisor and technical authority to C-level stakeholders Deep expertise in frontier GenAI/LLMs, including: RAG systems and vector databases Agentic AI and multi-agent systems Fine-tuning, prompt engineering, and in-context learning Evaluation frameworks and metrics for LLM systems Production MLOps and deployment Hands-on proficiency with cloud platforms (AWS, Azure, GCP) and modern AI/ML frameworks (PyTorch, TensorFlow, LangChain, Hugging Face) Proven track record of success in ambiguous, complex environments with a high degree of autonomy Excellent communication skills across technical, research, and executive audiences Self-directed and resourceful - comfortable operating independently at customer sites and leading without direct authority Customer-centric mindset with ability to build trust and credibility quickly with C-level stakeholders Bachelor's degree in Computer Science, Engineering, AI/ML, or related field Preferred Advanced degree (MS/PhD in Computer Science, AI/ML, or related field) Experience at AI platform/research companies (Anthropic, OpenAI, Palantir, Scale AI, Cohere, etc.) in research, Forward Deployed, or leadership roles Experience at top-tier consulting firms (McKinsey, Bain, Deloitte) in technical or engineering leadership roles Published technical work or recognized AI/ML expertise, contributing to thought leadership and industry recognition Prior work in regulated industries (financial services, healthcare) with compliance requirements and enterprise complexity Track record of mentoring engineers and building high-performing teams Computer vision and/or data annotation platform expertise Multilingual capabilities for global customer engagement Open-source contributions or active participation in AI research community Experience building bespoke solutions vs. configuring off-the-shelf products What Success Looks Like First 90 Days Successfully embedded at assigned customer account(s) Built trust and credibility with customer technical and executive leadership Conducted discovery and scoped research initiatives aligned with customer strategy Established team structure, processes, and success metrics for Forward Deployed team Delivered initial research insights, POC, or quick wins demonstrating value Established clear research roadmap and delivery milestones Year 1 Delivered production AI system(s) informed by applied research, driving measurable customer business outcomes Customer views TELUS Digital as indispensable technical and research partner Identified and scoped additional research and implementation opportunities within customer organization(s) Mentored customer AI teams and Forward Deployed team members, building their research and production capabilities Contributed technical and research insights that influenced TELUS Digital product/service offerings Built reusable patterns, frameworks, or accelerators benefiting other customers and Forward Deployed Engineers Established team as high-performing unit with strong retention and customer satisfaction Ongoing Consistently delivers high-quality, production-ready, research-backed AI solutions across multiple customer engagements Maintains deep, trusted relationships with customer technical and executive leadership Expands TELUS Digital footprint and influence within customer organizations Serves as technical authority and thought leader in AI research and enterprise implementation Mentors Forward Deployed Engineers, Solutions Engineers, and customer teams Contributes to TELUS Digital's thought leadership through publications, presentations, or research initiatives Drives continuous innovation in AI solution architecture and deployment methodologies Builds and scales high-performing Forward Deployed team with strong talent retention and career development Work Environment Onsite Presence Embedded at customer locations (Fortune 500 enterprises, banks, healthcare organizations, AI-native companies) Work within customer facilities alongside their engineering and research teams Adapt to customer culture, processes, and working styles Build deep relationships and trust through consistent presence and technical credibility Travel Expectations 60-80% travel to customer sites Extended onsite engagements (weeks to months) Occasional travel to TELUS Digital offices for team collaboration, research initiatives, and knowledge sharing Flexibility to support multiple customer locations as needed Customer Types Fortune 500 enterprises (Amazon, Microsoft, Apple, Google, and similar) AI-native companies and frontier AI research teams Highly regulated industries (financial services, healthcare) with complex AI requirements Organizations with mission-critical, research-intensive AI challenges Geographic Focus Customers primarily in major US metro areas (Seattle, Bay Area, Boston, New York, etc.) Key Competencies Technical Production software engineering (Python, TypeScript/JavaScript, SQL) Frontier GenAI/LLM systems (RAG, agentic AI, fine-tuning, evaluation, vector databases) Applied AI research methodologies and experimentation Cloud platforms and MLOps (AWS, Azure, GCP, Kubernetes, CI/CD) Data engineering, integration, and synthetic data generation System architecture, scalability, and performance optimization Model evaluation frameworks and metrics Research