Sr Architect

Job Summary

Sr Architect

Responsibilities

• AI-enabled solution architecture: 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.
• AI scalability, economics and operational readiness: 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.
• Automation advancement across core technology: 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.
• Architecture governance, security responsible AI: 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.
• Delivery leadership, collaboration knowledge transfer: 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.

Qualifications

• 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.
• Preferred qualifications: Experience in a regulated industry, healthcare, life sciences, or clinical and commercial technology environments.
• Preferred qualifications: Experience with enterprise architecture methods, product operating models, Agile delivery, architecture decision records, technology governance, and portfolio or lifecycle management.
• Preferred qualifications: Relevant architecture, cloud, data, AI, security, automation, Agile, IT service management, or FinOps certifications.

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