Description
Syneos Health is seeking a Sr. Architect, AI Enablement & Automation to lead end-to-end architecture for assigned AI-enabled and automation initiatives.
Job Responsibilities
- 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 & 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.
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.
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.
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.
QUALIFICATION REQUIREMENTS
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field; master’s degree preferred.
- 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.