Role Description Our Client is building an AI operations platform that enables enterprise organizations to automate complex end-to-end business processes through AI agents. Rather than simply assisting users, the platform executes real operational work across systems, workflows, and business functions. Starting with retail and consumer packaged goods (CPG) organizations, the company is helping enterprise teams eliminate manual processes, improve operational efficiency, and unlock measurable business outcomes. With strong traction and a rapidly growing customer base, Our Client is building a category-defining platform at the intersection of AI, automation, and enterprise operations. Key Responsibilities - Lead enterprise transformation initiatives for retail and CPG customers - Build executive-level business cases tied to measurable financial outcomes - Partner directly with COOs, CFOs, business unit leaders, and executive stakeholders - Define transformation roadmaps and prioritize high-impact operational opportunities - Drive adoption and organizational change across customer teams - Establish success metrics, baseline measurements, and ROI frameworks - Present transformation progress and business results to executive leadership and boards - Identify expansion opportunities and support enterprise-wide rollouts - Collaborate closely with product, engineering, and leadership teams - Act as the strategic bridge between technology capabilities and business value Qualifications - Experience within retail, FMCG, or consumer packaged goods organizations - Background in a top-tier consulting firm (MBB, Big 4, or equivalent) - Experience leading business transformation initiatives - Proven ability to engage and influence C-level stakeholders - Strong commercial and financial acumen - Experience developing business cases and outcome measurement frameworks - Track record of driving operational change and organizational adoption - Ability to work independently in a high-growth environment Preferred Qualifications - Experience leading multi-country or multi-business-unit transformation programs - Exposure to automation, workflow platforms, AI, or digital transformation initiatives - Experience with retail operations, category management, supply chain, or finance operations - Additional European language skills (French, Czech, Nordic languages) - Experience scaling pilots into enterprise-wide programs Benefits - Competitive compensation package (€120,000 - €250,000) - Meaningful equity participation - Unlimited AI tooling budget - High level of ownership and autonomy - Opportunity to help define a new category in enterprise AI - Direct collaboration with founders and executive leadership - Exposure to some of the most innovative retail and CPG organizations - Flexible remote working environment - Significant long-term growth opportunities

• Architect and evolve our cloud-native platform and services to support high-throughput, low-latency data processing patterns, customer-facing features, and design platform to meet scalability requirements. • Design scalable, distributed systems powering complex capabilities such as authentication & authorization, data lifecycle management, metadata management, operational intelligence, and real-time event processing. • Evolve the authorization service toward modern identity standards and customer-configurable, fine-grained access models that scale without a release for every new role including authorization for non-human identities (service-to-service, AI agents, MCP-based tooling). • Built systems that capture and enforce structured metadata at ingest and serve it through clean service contracts; understands where platform metadata plumbing ends and the semantic/ontology layer begins, and collaborates well across that boundary. • Build governance primitives for a regulated environment — compliance-grade audit trail, dataset-level access controls, and approval / eSignature workflows. • Collaborate with engineering and product teams to deliver infrastructure that supports new services, customer-facing applications, and high-volume data processing workloads. • Build and maintain infrastructure-as-code (e.g., CloudFormation, AWS CDK) to automate, standardize, and secure deployments to support online upgrades and on-demand infrastructure allocation. • Enhance observability and monitoring to ensure reliability, cost efficiency, and rapid incident response. • Champion best practices in distributed systems design, scalability, and performance optimization, and share architectural insights through design reviews and technical documentation.

• Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services. • Real-Time Inference at Scale: Build automation for deploying low-latency services to serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults. • Feature Engineering & Data Strategy: You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains. • End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.

• Own the technical architecture of the data platform end-to-end: ingestion, storage, transformation, orchestration, serving, and observability layers. • Author and maintain the platform architectural vision document; lead quarterly architecture reviews to assess alignment with organizational goals and technology trends. • Define and evolve the target-state architecture for the platform, establishing a multi-year technology roadmap in partnership with engineering leadership. • Evaluate emerging technologies, frameworks, and make evidence-based adoption recommendations. • Serve as the final technical escalation point for complex design questions, cross-team conflicts, and build-vs-buy decisions affecting the platform. • Lead the planning, execution, and delivery of multi-quarter platform initiatives involving multiple engineers, cross-functional dependencies, and significant organizational impact. • Break down large, ambiguous programs into scoped workstreams; assign technical leads per workstream, define milestones, and manage cross-team dependencies and risk. • Drive initiative kick-offs with clear problem framing, success criteria, architectural constraints, and delivery phasing — from 0-to-1 exploration through production hardening. • Maintain stakeholder alignment throughout delivery: proactively communicate status, surface trade-offs, and escalate blockers to leadership before they become risks. • Lead post-mortems and retrospectives for large initiatives; document and socialize lessons learned to raise the organizational bar on delivery excellence. • Exercise functional oversight across the EDAP team: review technical designs, set quality gates, approve architectural decisions, and ensure consistency of implementation patterns. • Define, document, and socialize platform engineering standards including coding conventions, testing requirements, CI/CD practices, schema design guidelines, and SLA frameworks. • Establish and own the platform's technical review process (design review): triage incoming projects, chair review sessions, and ensure decisions are documented and traceable. • Identify and drive resolution of technical debt, redundancy, and architectural drift that impede platform reliability, developer productivity, or scalability. • Partner with security, compliance, and infrastructure teams to ensure platform systems meet governance, data privacy, and regulatory requirements by design. • Serve as a primary technical mentor for junior and senior data platform engineers across the platform; provide structured coaching on system design, technical communication, and engineering judgment. • Conduct and lead design reviews, architecture critiques, and technical deep-dives that strengthen the overall capability of the engineering team. • Define what technical excellence looks like at each level of engineers and participate in calibration discussions. • Represent the data platform in engineering-wide forums, all-hands, and external venues (conferences, open-source communities, recruiting events). • Build a culture of documentation, reliability, and platform-as-a-product thinking across all data platform engineering functions. • Participate in goal planning cycles as a technical voice; define engineering-led goals that improve platform reliability, developer experience, and data quality.