Senior AI Architect

The Senior AI Architect will lead the ITSOP AI strategy and translate it into secure, scalable, and production-ready AI solutions. The role involves hands-on design, prototyping, and guiding engineering teams to deploy enterprise-scale Generative AI and agentic solutions.

Responsibilities

• Develop the ITSOP AI strategy, capability roadmap, reference architectures, standards, and reusable patterns, aligned with enterprise direction and measurable operational outcomes.
• Translate business opportunities into prioritized AI use cases, target architectures, delivery approaches, and investment recommendations.
• Design end-to-end solutions spanning user experience, agents, models, retrieval, enterprise data, APIs, identity, security, observability, and operational support.
• Evaluate technology choices and architecture trade-offs across models, platforms, orchestration frameworks, retrieval approaches, and integration patterns.
• Design, code, test, and demonstrate working prototypes and reference implementations using Python and modern software engineering practices.
• Graduate selected solutions from discovery and proof of concept through technical validation, production design, deployment, and operational readiness.
• Create reusable SDKs, templates, components, and implementation guidance that enable delivery teams to build consistently and securely.
• Conduct design and code reviews, troubleshoot complex cross-layer issues, and provide practical technical direction to engineering teams.
• Design and implement agentic solutions using tool calling, structured outputs, orchestration, state and memory management, routing, retries, fallbacks, and human approval patterns.
• Design safe single-agent and multi-agent workflows with clear task boundaries, controlled permissions, escalation paths, and failure handling.
• Build Retrieval-Augmented Generation and enterprise knowledge solutions using semantic search, vector retrieval, knowledge graphs, ontologies, and structured and unstructured data.
• Integrate agents with enterprise applications, APIs, event-driven services, data platforms, content repositories, and business workflows.
• Apply open agent interoperability approaches such as Model Context Protocol (MCP), Agent2Agent (A2A), or equivalent standards where appropriate.
• Define and implement evaluation frameworks covering groundedness, relevance, completeness, task success, safety, latency, reliability, and cost.
• Create automated regression tests, representative evaluation datasets, and human-review processes for prompts, retrieval, tools, models, and agent behavior.
• Implement end-to-end tracing, prompt and configuration versioning, model and tool telemetry, cost monitoring, feedback capture, release controls, and rollback approaches.
• Apply AI-specific security controls for prompt injection, data leakage, unsafe tool execution, sensitive information, identity, authorization, auditability, and workload isolation.
• Embed Responsible AI, privacy, records management, compliance, and human oversight requirements throughout the solution lifecycle.
• Act as a trusted technical advisor while remaining directly involved in prototyping, implementation decisions, and production problem-solving.
• Partner with business, product, architecture, data, security, platform, and engineering teams to deliver measurable value and sustainable adoption.
• Mentor architects and engineers, share implementation practices, and raise the unit’s capability to deliver governed AI solutions.
• Monitor emerging AI technologies and recommend adoption only where they provide clear business, engineering, or operational value.

Requirements

• Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence/Machine Learning, Information Systems, or a related field, with a minimum of 8 years of relevant professional experience; or Bachelor's degree with a minimum of 10 years of relevant professional experience; or an equivalent combination of education and experience.
• Demonstrated experience designing, developing, deploying, and supporting enterprise-scale Generative AI and/or Agentic AI solutions in production environments.
• Proven experience translating business requirements into scalable technology solutions and delivering measurable business outcomes.
• Experience working with enterprise data, APIs, applications, workflow platforms, and knowledge management systems.
• Strong software engineering skills, including Python development and modern software delivery practices.
• Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agent frameworks, and AI solution architecture.
• Experience designing and implementing AI-enabled search, retrieval, document intelligence, and knowledge-grounded solutions.
• Knowledge of responsible AI principles, including security, privacy, governance, compliance, explainability, and risk management considerations.
• Demonstrated ability to communicate complex technical concepts and architectural decisions to both technical and non-technical stakeholders.
• Strong analytical, problem-solving, collaboration, and stakeholder management skills, with the ability to work effectively across multidisciplinary and globally distributed teams.
• Proven client focus, sound judgment, and commitment to delivering practical and measurable business outcomes.
• Demonstrated experience designing, implementing, operating, and continuously improving enterprise AI solutions in production environments across the full solution lifecycle.
• Preferred experience with enterprise AI platforms such as Azure OpenAI, AWS Bedrock, Azure AI Foundry, Microsoft Copilot Studio, Databricks Mosaic AI, or similar technologies.
• Experience implementing AI solutions within regulated or security-conscious environments.
• Experience developing reusable frameworks, reference architectures, standards, templates, or developer enablement assets.
• Relevant AI, cloud, architecture, security, or DevOps certifications.
• Experience designing knowledge-grounded AI solutions and reusable enterprise AI capabilities that support knowledge-intensive business processes, decision-making, and the safe, scalable adoption of AI.

Skills

• Generative AI
• Agentic AI Solutions
• Enterprise AI Architecture
• Python Development
• Large Language Models
• Retrieval-Augmented Generation
• AI Solution Architecture
• AI-Enabled Search
• Document Intelligence
• Knowledge-Grounded AI
• Responsible AI Principles
• AI Security and Privacy
• AI Governance and Compliance
• AI Explainability
• Risk Management
• Enterprise Data Initiatives
• API Integration
• Workflow Platform Integration
• Knowledge Management
• Software Engineering
• Cloud AI Platforms
• Azure OpenAI
• AWS Bedrock service
• Microsoft Copilot
• Databricks Mosaic AI
• AI Solution Lifecycle
• Reusable AI Frameworks
• Reference Architecture
• Developer Enablement
• AI in Regulated Environments
• AI DevOps
• AI Certifications

Languages

English