AI HeadCPG | Marketplace & RetailROLE CONTEXT & STRATEGIC RATIONALEThe Head of AI is a newly scoped leadership role designed to unify the company's technology function across three high-growth verticals. This is not a purely functional or delivery role. It is a business-critical leadership position that sits at the intersection of AI engineering, enterprise software, and client value creation. The role is being created to address three key imperatives: • Establish a unified, scalable AI and engineering practice that spans CPG, Marketplace & Retail, and Industrial verticals, moving from bespoke builds to reusable, enterprise-grade platforms. • Provide senior technical authority that talented Tech Leads, Data Engineers, and Data Scientists can rally behind by setting direction, standards, and culture. • Build deep technology-business fluency by translating client business problems into scalable software solutions, not just technical deliverables.
ROLE OVERVIEW:
Reporting directly to the C-level executive responsible for the CPG, Retail & Industrials, the Head of AI will own the AI mandate for the CPG, Retail, Marketplace, and Industrials business group, partnering closely with an offshore counterpart. Key areas of ownership include:
- AI practice development
- Solution architecture
- Enterprise coding standards
- Security frameworks
- Productionization of AI and analytics solutions at client scale This role also requires exceptional people leadership, including building, inspiring, and retaining a multidisciplinary technology team while serving as the primary technical voice in senior client conversations. KEY RESPONSIBILITIESClient Centricity & Business Engagement
- Serve as the senior technical voice in C-suite client conversations, translating complex technology capabilities into business outcomes and ROI narratives.
- Partner with Account and Business Development teams during pre-sales activities, including solution scoping, effort estimation, technical proposal writing, and client presentations.
- Develop a deep understanding of the business dynamics within CPG, Marketplace & Retail, and Industrial sectors.
- Build trusted advisor relationships with CTOs, CDOs, and VP Engineering stakeholders.
- Participate in QBRs, steering committees, and innovation workshops with Tier-1 clients to position the company’s technology vision and roadmap. Team Leadership & People Development
- Lead multidisciplinary teams of Tech Leads, Senior Software Engineers, Data Engineers, and Data Scientists.
- Act as the technical north star, providing clarity, mentorship, and engineering direction.
- Attract, hire, and retain senior engineering talent while strengthening the company’s technical brand.
- Facilitate architecture forums, tech talks, and learning programs to keep teams current on AI/ML and software engineering advancements.
- Partner with Delivery leadership on resource planning, capacity management, and talent allocation. AI Practice & Agentic Solution Development
- Own the strategic roadmap for the AI Practice across all business verticals.
- Lead the design and delivery of agentic AI solutions, including autonomous agents, multi-agent orchestration systems, and LLM-powered workflows.
- Evaluate and implement modern AI frameworks such as:o LangGrapho AutoGeno CrewAIo OpenAI Assistants
- Establish governance around:o Model registrieso Prompt engineering standardso Responsible AI practices
- Drive thought leadership through publications, client workshops, perspectives, and industry engagement. Solution Architecture & Design
- Serve as the chief architect across major client engagements.
- Own architecture from data ingestion through model deployment and business reporting.
- Define and enforce standards for:o Cloud-native architectureso Microserviceso API-first designo Lakehouse architectureso Medallion data platforms
- Lead architecture review boards to ensure designs are scalable, cost-optimized, and production-ready.
- Build reusable accelerators, reference architectures, and intellectual property assets.
- Partner with Data Science teams to establish:o MLOps pipelineso Feature storeso Model training environmentso Monitoring and drift detection frameworks Productionizing Solutions at Scale
- Drive organizational maturity in moving AI and analytics solutions from proof of concept to enterprise production.
- Own platform engineering capabilities, including:o Kuberneteso Terraformo AWS CDKo Observability platformso Site Reliability Engineering (SRE) practices
- Define:o SLAso SLOso Operational runbooks
- Establish productionization frameworks covering:o Feature engineering workflowso Real-time and batch model servingo Monitoring and observabilityo Human-in-the-loop processes
- Collaborate with Delivery and Client Success teams to support cloud, hybrid, and on-premises deployments. Enterprise-Grade Coding Practices
- Define and institutionalize software engineering standards across the organization.
- Establish best practices for:o Code reviewso Branching strategieso CI/CD pipelineso Automated testing
- Champion:o Clean code principleso SOLID design patternso Test-driven developmento Documentation standards
- Implement inner-source practices that promote code sharing and reuse.
- Standardize technology stacks to improve maintainability and reduce fragmentation.
- Introduce engineering productivity metrics such as:o DORA Metricso Deployment Frequencyo Cycle Timeo Change Failure Rate Enterprise Security & Privacy Standards
- Own and enforce enterprise security standards across all client-facing solutions.
- Establish security-by-design principles, including:o Threat modelingo Vulnerability assessmentso Dependency scanningo Secure coding practices
- Lead compliance alignment efforts for:o SOC 2 Type IIo ISO 27001o Additional regulatory frameworks as required
- Define privacy-preserving practices, including:o Data anonymizationo Pseudonymizationo Differential privacyo Data residency controls
- Govern AI-specific security considerations, including:o Prompt injection preventiono Model access controlso Data leakage protectiono Adversarial robustnessQUALIFICATIONS & EXPERIENCEEssential
- 15+ years of progressive technology experience.
- Minimum 5 years in a senior engineering leadership role such as:o Engineering Directoro VP Engineeringo Head of AIo Equivalent leadership positions
- Proven experience architecting and delivering enterprise-scale AI/ML or data solutions.
- Deep expertise with at least one major cloud platform:o AWSo Azureo GCP
- Experience leading multidisciplinary teams of 30+ engineers, data engineers, and data scientists.
- Strong understanding of:o CI/CDo DevOpso Infrastructure as Codeo Containerizationo API Design
- Hands-on experience with:o LLMso Generative AI frameworkso Agentic architectureso Production AI deployments
- Experience establishing enterprise security standards and compliance frameworks such as:o SOC 2o GDPRo Equivalent frameworks
- Exceptional communication and stakeholder management skills. Preferred
- Experience in one or more of the following industries:o CPGo Retailo E-commerceo Marketplaceo Industrialo Manufacturing
- Experience within a global consulting, data analytics, or technology services organization.
- Exposure to:o Pre-saleso Solution commercializationo Proposal developmento Effort estimationo Technical storytelling
- Advanced degree (M.Tech, MS, or equivalent) in:o Computer Scienceo Engineeringo Related quantitative discipline
- Published thought leadership, conference speaking experience, or open-source contributions in AI/ML engineering.
Our client is an Equal Opportunity Employer, committed to a workplace free from discrimination and harassment. Employment decisions are based on business needs, job requirements, and individual qualifications without regard to race, color, religion, gender, age, disability, sexual orientation, gender identity, marital status, military service, genetic information, or any other status protected by law.
