Job Description

We are seeking an experienced AI Architect to design, govern, and scale end-to-end AI solutions that deliver measurable business outcomes. This role sits at the intersection of data, machine learning, engineering, and product, translating business needs into robust, secure, and scalable AI architectures.

The AI Architect will define reference architectures, select platforms and tools, and guide teams in building production-grade AI systems across the enterprise.

Key Responsibilities

Platform Architecture & Vision

Own the end-to-end architecture for the AI platform, spanning:Agent frameworks and orchestration layersSemantic and knowledge graph foundationsData and signal ingestion fabricModel, reasoning, and tool-execution servicesProduct and solution enablement layersEstablish modular, extensible reference architectures enabling rapid product and solution development.Drive architectural consistency across teams building on AI Platform.

2.Agentic & Knowledge-Driven AI Systems

Architect agent-based systems capable of reasoning, planning, retrieval, and execution across enterprise workflows.Design hybrid AI architectures combining:LLMs and multi-model stacksKnowledge graphs and ontologiesVector retrieval and semantic searchDeterministic services and enterprise APIsLead the evolution of CINDE’s semantic layer and retail knowledge foundation.

3. Solution Architecture & Business Enablement

Partner with Product, Engineering, and Business leaders to translate strategy into scalable technical systems.Architect AI solutions across retail and CPG domains, including:Forecasting, demand intelligence, and optimizationPrice, promotion, and assortment intelligenceShopper personalization and retail mediaStore, shelf, and inventory intelligenceEnterprise revenue and decision automationEnsure architectures directly support revenue growth, product velocity, operational efficiency, and customer impact.

4. AI Platform Engineering, MLOps & LLMOps

Define CINDE standards for:Model lifecycle managementAgent deployment and orchestrationPrompt, workflow, and tool governanceExperimentation and evaluation pipelinesDesign scalable MLOps / LLMOps / AgentOps foundations:CI/CD for AI and agent workflowsObservability, telemetry, and quality measurementVersioning, monitoring, drift detection, and retraining

5. Governance, Security & Responsible AI

Embed enterprise-grade security, privacy, and compliance into CINDE architecture.Define and enforce Responsible AI frameworks across the platform:Explainability, traceability, and auditabilityBias, safety, and risk controlsRegulatory and customer-facing compliance readinessPartner closely with Security, Legal, and Compliance leaders.

6. Technical Leadership & Influence

Serve as a technical north star across product and engineering organizations.Mentor senior engineers, architects, and data scientists.Influence platform decisions across multiple business units without direct authority.Continuously assess emerging technologies and translate them into advantage.

Required Technical Skills

Cloud & Platform Engineering

Deep experience with AWS, Azure, or GCP AI platformsKubernetes, containerized AI workloads, and distributed systemsInfrastructure as Code and environment automation

Data, Knowledge & Signal Fabric

Enterprise data lakes and lakehouse platformsStreaming and real-time signal architecturesStrong distributed data processing backgroundKnowledge graph platforms, semantic modeling, and ontologies

AI, ML & Agentic Systems

Expert-level PythonProduction ML frameworks (PyTorch, TensorFlow, scikit-learn)Agent frameworks and orchestration platformsMulti-model system design

GenAI & Knowledge-Grounded AI

Commercial and open-source LLM ecosystemsRAG and hybrid retrieval architecturesVector databases and embedding systemsFine-tuning, evaluation, and prompt lifecycle managemen

MLOps / LLMOps / AgentOps

MLflow, Kubeflow, or equivalent platformsCI/CD for AI workloadsModel and agent observability, testing, and governance