AI Architect

Job Summary We are seeking an AI Architect to design and deliver enterprise AI platforms and solutions across modern cloud environments. This role is responsible for architecting and implementing advanced AI capabilities including Retrieval-Augmented Generation (RAG) systems, agentic AI frameworks, document intelligence services, and responsible AI solutions on Azure platforms. The ideal candidate will bridge strategy and execution by defining enterprise AI architectures, developing rapid prototypes, writing production-grade code, and collaborating with business, security, and cloud engineering teams. Key Responsibilities Design and implement end-to-end AI architectures including RAG pipelines, agentic orchestration frameworks, retrieval strategies, grounding mechanisms, and structured outputs. Architect enterprise document intelligence solutions including ingestion pipelines, OCR/vision processing, extraction workflows, verification mechanisms, multi-tenant architecture, and sensitive data handling. Define enterprise AI patterns including prompt governance, model routing, security controls, human-in-the-loop (HITL) workflows, plugin/MCP integrations, and verification/fallback strategies. Develop rapid proof-of-concept (POC) solutions in Python to validate architecture decisions and accelerate project delivery. Lead TrustAI initiatives including hallucination testing, bias and fairness evaluations, lineage tracking, scoring, drift monitoring, and compliance alignment. Design and implement telemetry and observability frameworks for prompts, retrieval tracing, evaluation feedback loops, and guardrail instrumentation using tools such as OpenTelemetry, Langfuse, and Application Insights. Collaborate with Information Security and Cloud Enablement teams to implement secure AI architectures including private networking, identity boundaries, policy-as-code, data loss prevention (DLP), and model risk governance. Publish reference architectures, reusable AI modules, technical standards, and implementation playbooks. Lead architecture reviews, mentor engineering teams, and provide technical leadership across AI initiatives. Present technical solutions and AI strategies to executive leadership, engineering teams, and security stakeholders. Required Qualifications Minimum of 10 years of experience in software engineering and/or machine learning engineering. Strong proficiency in Python for APIs, services, automation, and production-grade application development. Deep expertise in Retrieval-Augmented Generation (RAG) architectures including embeddings, hybrid search, chunking, reranking, evaluations, and guardrails. Experience designing and implementing agentic AI systems including multi-step agents, tool calling, and orchestration frameworks such as LangChain, LlamaIndex, or custom frameworks. Experience with MCP/plugin architectures, tool-server integrations, and context protocol design. Strong experience with Azure services including Azure OpenAI, Azure AI Search, Azure Machine Learning, AKS, Azure Functions, Key Vault, and VNET/private endpoint configurations. Solid understanding of LLM lifecycle management including prompting strategies, structured outputs, token optimization, cost management, and fine-tuning concepts. Experience designing AI systems for regulated industries with focus on PII handling, security isolation, auditing, and governance. Strong communication and presentation skills with the ability to engage technical teams, executive leadership, and security stakeholders. Preferred Qualifications Experience collaborating with large enterprise application development and delivery teams in complex environments. Experience working with senior leadership and cross-functional technical teams. Experience with LLM fine-tuning techniques such as LoRA and QLoRA. Experience with evaluation frameworks including RAGAS and pairwise evaluation methodologies. Experience with vector databases such as Milvus, Pinecone, or Weaviate. Experience implementing TrustAI solutions including governance dashboards, safety evaluations, and drift monitoring. Experience with TypeScript and Node.js for plugin development or front-end integrations.