SCT_Tech Enablement_AI Architect_Manager

The opportunity

AI Architect- SC products: We are seeking an experienced Supply Chain AI Solution Architect to join our Digital Supply Chain Team. This hybrid role combines enterprise AI architecture, Supply Chain taxonomies and knowledge graph, product thinking, and client advisory to translate business needs into secure, scalable solutions that deliver measurable value.

Your key responsibilities

• Collaborate with business stakeholders, product owners, and technical teams to identify AI opportunities and translate requirements into scalable AI solution architectures.
• Design and architect enterprise-grade AI applications leveraging LLMs, RAG, Agentic AI, Knowledge Graphs, GraphRAG, and Multimodal AI patterns.
• Define end-to-end AI solution architectures, including model selection, data retrieval strategy, orchestration frameworks, deployment models, and integration patterns.
• Design and implement advanced prompting, context engineering, structured outputs, tool calling, workflow orchestration, and memory management strategies.
• Architect hybrid retrieval solutions using vector search, semantic search, GraphRAG, SQL-based retrieval, and vectorless approaches.
• Design and implement MCP-based integrations with enterprise applications, APIs, databases, and business systems.
• Establish AI platform standards covering observability, evaluation frameworks, LLMOps, monitoring, governance, performance optimization, and cost management.
• Define AI security architectures addressing prompt injection, data leakage, unauthorized tool access, jailbreak attempts, and AI-specific threats.
• Ensure compliance with Responsible AI principles, privacy regulations, governance requirements, and security standards.
• Conduct architecture reviews, technical assessments, proof-of-concepts, and production-readiness evaluations.
• Drive AI adoption across business and technology teams through workshops, technical leadership, and stakeholder engagement.
• Mentor AI engineers, architects, and developers while defining best practices, reusable frameworks, and reference architectures.
• Stay current with emerging AI technologies, frameworks, and industry trends to drive continuous innovation.

To qualify for the role, you must have

• 12+ years of experience in software engineering, cloud architecture, data engineering, enterprise architecture, or related technology domains.
• Strong experience designing and implementing enterprise AI, Generative AI, and intelligent automation solutions.
• Hands-on experience with Large Language Models (LLMs), prompt engineering, context engineering, RAG, embeddings, reranking, and vector databases.
• Strong knowledge of AI agent architectures, multi-agent systems, tool calling, memory strategies, and human-in-the-loop workflows.
• Proficiency in Python, API development, microservices architecture, and distributed systems.
• Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform and their AI services.
• Strong understanding of databases, data platforms, event-driven architectures, and enterprise integration patterns.
• Practical experience with MCP, REST APIs, JSON-RPC, OAuth, and secure integration frameworks.
• Knowledge of AI evaluation frameworks, tracing, monitoring, observability, LLMOps, and AI cost optimization.
• Understanding of model fine-tuning, LoRA/QLoRA, model routing, quantization, and inference optimization techniques.
• Experience with multimodal AI, knowledge graphs, GraphRAG, SQL agents, and advanced retrieval architectures.
• Excellent communication, stakeholder management, architecture documentation, and leadership skills.
• Strong analytical and problem-solving capabilities with a focus on delivering business outcomes.

Ideally, you'll also have

• Experience with frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, or equivalent orchestration platforms.
• Experience working with OpenAI, Anthropic, Google Gemini, Azure AI, AWS Bedrock, Hugging Face, or similar AI ecosystems.
• Familiarity with vector databases and search platforms such as Pinecone, Qdrant, Weaviate, Milvus, pgvector, Elasticsearch, OpenSearch, or Neo4j.
• Experience with Docker, Kubernetes, MLflow, vLLM, Langfuse, Ragas, DeepEval, and AI platform engineering practices.
• Understanding of AI governance and security frameworks, including NIST AI RMF, OWASP GenAI Security, ISO/IEC 42001, and cloud security best practices.
• Experience leading enterprise AI transformation initiatives, Centers of Excellence (CoE), or AI capability building programs.
• Exposure to MLOps.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.