Sr Architect

· Lead end-to-end architecture for assigned AI-enabled and automation initiatives, spanning applications, data and knowledge, models, orchestration, agents, APIs, integration, cloud services, identity, security, observability, and operations.

· Translate business outcomes, functional needs, non-functional requirements, and regulatory obligations into architecture options, technical designs, and implementation guidance.

· Design for production scalability, resilience, performance, interoperability, maintainability, portability, and reuse, including workload sizing, capacity assumptions, failure modes, service limits, and operational dependencies.

· Define and develop practical patterns for generative AI, machine learning, intelligent workflows, agentic automation, retrieval and context, tool integration, human oversight, and exception handling when relevant to the project.

· Make cost a first-class architecture concern by assessing consumption drivers such as model usage, tokens, compute, storage, data movement, licensing, vendor services, and support effort.

· Create cost and capacity scenarios for prototype, launch, and scaled adoption; identify material cost-performance-quality trade-offs; and recommend fit-for-purpose models, hosting patterns, caching, routing, batching, quotas, and scaling controls.

· Embed telemetry, usage metering, cost attribution, budgets or thresholds, performance monitoring, quality evaluation, and operational alerts into the solution design so teams can make evidence-based scale, optimize, or stop decisions.

· Partner with platform, FinOps, operations, and product stakeholders to establish ownership, service expectations, support models, lifecycle controls, and continuous optimization practices before production adoption.

· Evaluate build, buy, reuse, and retire options using business value, total cost, architecture fit, delivery risk, security, compliance, vendor dependency, and long-term operability.

· Identify and shape high-value automation opportunities across Enterprise Architecture and Enterprise Technology & Platforms, to streamline, simplify and elevate intelligence related to complex technical service delivery processes.

· Advance automation-as-a-capability by designing reusable workflows, APIs, events, templates, policy-as-code, architecture-as-code, and paved-road patterns that reduce manual effort and improve consistency.

· Lead focused proofs of value and production pilots with clear hypotheses, success measures, guardrails, and exit criteria; convert validated learning into reusable patterns, backlog recommendations, standards, or scaled implementation plans.

· Promote human-centered automation that preserves appropriate review, accountability, transparency, and fallback paths for material decisions and regulated processes.

· Apply enterprise architecture principles, approved technology standards, security and privacy requirements, data governance, responsible AI expectations, and regulatory controls throughout the delivery lifecycle.

· Apply project guardrails for data handling, model and provider use, identity and access, content safety, prompt and configuration management, intellectual property, auditability, evaluation, monitoring, human oversight, and incident response.

· Facilitate architecture reviews, document decisions and exceptions, surface material risks and dependencies early, and drive accountable remediation with delivery owners.

· Assess emerging technologies and vendors through structured experiments and architecture reviews, separating credible enterprise value from unsupported novelty.

· Provide architectural leadership at the project level throughout discovery, planning, design, backlog refinement, implementation, testing, release, and transition to operations; remain engaged to validate that delivered solutions conform to approved architecture.

· Collaborate closely with solution architects, engineers, data teams, platform teams, security, operations, product managers, program leaders, and business stakeholders to resolve cross-domain decisions and delivery constraints.

· Communicate complex technology choices in clear business terms, including value, cost, risk, quality, scalability, and delivery implications; present recommendations to architecture and project governance forums.

· Collaborate across teams to develop reusable reference architectures, patterns, templates, decision guidance, and implementation assets; contribute lessons learned to the Enterprise Architecture community of practice.

· Mentor architects and engineers through project work, design reviews, pairing, and knowledge sharing, while retaining the role’s individual-contributor focus.

· Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field; master’s degree preferred. Equivalent experience, skills, and education will be considered.

· Minimum of 5 years of experience in software, solution, enterprise, cloud, platform, data, or AI architecture, with demonstrated delivery of large-scale or business-critical systems.

· Experience designing and guiding complex solutions across cloud, applications, data, APIs, integrations, security, and operational environments.

· Working knowledge of modern AI architecture, including generative AI, model and provider selection, retrieval and context patterns, agents and tool use, MLOps or LLMOps, evaluation, observability, and responsible AI controls.

· Demonstrated ability to design for scalability and reliability and to analyze technology economics, consumption-based services, capacity, total cost, and cost-performance-quality trade-offs.

· Experience advancing automation through APIs, workflow platforms, event-driven integration, infrastructure or policy as code, platform engineering, DevSecOps, or similar methods.

· Strong problem-solving, facilitation, communication, and documentation skills, with the ability to influence across functions and explain technical decisions to both engineering and business audiences.

· Ability to work independently, lead project architecture through ambiguity, manage competing constraints, and drive decisions and outcomes without relying on direct authority.