Serve as a senior executive advisor to client C-suite and technology leaders on AI-driven transformation, grounded in real-world delivery experience.
Lead strategic conversations on AI/ML architecture, platform modernization, model lifecycle management, governance, risk, and responsible AI.
Move beyond vision-setting — help clients define how AI systems will be architected, operationalized, scaled, and governed in complex enterprise environments.
Provide credible guidance on algorithmic design, model performance, data strategy, explainability, and AI risk management.
Partner with Client Partners, GTM, and Delivery leaders to originate, shape, and scale Data & AI engagements across North America.
Translate executive AI strategy discussions into structured, commercially viable programs and delivery roadmaps.
Identify high-value AI use cases aligned to measurable business outcomes (revenue growth, risk reduction, operational efficiency).
Actively contribute to revenue growth, pipeline expansion, and strategic account development.
Represent Endava’s Data & AI point of view in executive briefings, industry forums, conferences, and client workshops.
Inspire confidence and excitement about the potential of AI through compelling storytelling grounded in practical experience.
Work across sectors while bringing depth in Banking & Financial Services OR TMT.
Requirements
15+ years in Data & AI, with a proven track record of architecting and delivering production-grade AI/ML solutions, not just advising on strategy.
Deep technical foundation in AI/ML including algorithms, statistical modeling, optimization, and model lifecycle management.
Hands-on experience designing scalable AI architectures across cloud platforms (AWS, GCP, Azure) and modern data ecosystems.
Prior experience leading AI engineering, data science, or applied AI teams.
Academic or equivalent foundation in computer science, mathematics, engineering, statistics, or related discipline.
Demonstrated fluency in programming languages such as Python, Scala, or similar, and familiarity with ML frameworks and data engineering stacks.
Deep understanding of model performance, risk, bias, explainability, and AI governance.
Passionate advocate for AI’s transformative potential — able to energize clients and teams around what’s possible.
Visible thought leadership: public speaking, publications, executive briefings, or industry representation.