AI Head

AI Head
CPG | Marketplace & Retail

ROLE CONTEXT & STRATEGIC RATIONALE

The 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 RESPONSIBILITIES

Client 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 LangGraph
o AutoGen
o CrewAI
o OpenAI Assistants

• Establish governance around:

o Model registries
o Prompt engineering standards
o 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 architectures
o Microservices
o API-first design
o Lakehouse architectures
o 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 pipelines
o Feature stores
o Model training environments
o 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 Kubernetes
o Terraform
o AWS CDK
o Observability platforms
o Site Reliability Engineering (SRE) practices

• Define:

o SLAs
o SLOs
o Operational runbooks

• Establish productionization frameworks covering:

o Feature engineering workflows
o Real-time and batch model serving
o Monitoring and observability
o 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 reviews
o Branching strategies
o CI/CD pipelines
o Automated testing

• Champion:

o Clean code principles
o SOLID design patterns
o Test-driven development
o 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 Metrics
o Deployment Frequency
o Cycle Time
o 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 modeling
o Vulnerability assessments
o Dependency scanning
o Secure coding practices

• Lead compliance alignment efforts for:

o SOC 2 Type II
o ISO 27001
o Additional regulatory frameworks as required

• Define privacy-preserving practices