Architect, Delivery Operations & AI Systems

Job Description This role will serve as the lead Architect for Delivery Operations, designing and governing enterprise‑grade platforms, workflows, and integration patterns that enable scalable, secure, and reliable delivery of software, data, and AI‑enabled solutions across R&D and Safety. The role is responsible for defining architectural strategy, guiding implementation teams, and ensuring agentic, cloud, and enterprise application capabilities are translated into business value while meeting operational, compliance, and security standards. The Architect will act as a senior technical leader across product, engineering, operations, and data disciplines to modernize delivery capabilities, improve resilience, and establish scalable patterns for AI‑enabled systems and traditional application ecosystems. Responsibilities Architect enterprise delivery solutions for complex business and scientific workflows, including software platforms, automation pipelines, integrations, and AI‑enabled operational capabilities. Define end‑to‑end architecture patterns for agentic and traditional systems, including orchestration, tool integration, event‑driven services, APIs, cloud‑native components, and enterprise data connectivity. Lead design of LLM‑ and agent‑based capabilities, including prompting/orchestration strategies, memory and context handling, retrieval‑augmented generation (RAG), understanding of Model Context Protocol (MCP) structures, and human‑in‑the‑loop decision pathways. Establish architecture guardrails for safety, security, compliance, observability, reliability, and cost management across AI and application delivery platforms. Provide senior technical consultation to Delivery Operations leadership, product teams, software engineers, and business stakeholders on architecture decisions, technology selection, and implementation tradeoffs. Design for operational resilience, including monitoring, telemetry, fallback mechanisms, access control, prompt injection protection, and service recovery patterns. Drive cloud, DevOps, and MLOps architecture standards, including CI/CD pipelines, containerization, serverless workloads, message‑based processing, and scalable runtime environments. Evaluate emerging technologies and translate them into pragmatic, business‑relevant architecture recommendations that improve productivity, delivery speed, quality, and risk management. Lead cross‑functional architecture alignment across product management, software engineering, data science, platform teams, and support organizations to ensure cohesive technical direction. Mentor engineers and technical leads by promoting architecture best practices, reviewing designs, and helping teams solve highly complex implementation and operational challenges. Core Agentic & AI Skills LLM Orchestration & Prompting: Deep understanding of how LLMs plan, reason, and act in enterprise workflows. Tool & API Integration: Designing systems where agents safely invoke function‑calling patterns and external/internal APIs. Memory & Context Management: Architecting short‑term and long‑term memory strategies, including vector and graph‑based context persistence. Retrieval‑Augmented Generation (RAG)/Model Context Protocols (MCP): Connecting agents to enterprise knowledge and data sources securely, accurately, and with traceability. System Reliability & Governance Observability & Telemetry: Designing monitoring patterns for probabilistic systems, token consumption, latency, costs, and operational quality. Safety, Guardrails & Security: Implementing prompt protection, authorization controls, policy enforcement, fallback handling, and safe execution boundaries. Human‑in‑the‑Loop Design: Creating escalation and approval pathways where human review is required for sensitive or high‑risk decisions. Enterprise Architecture Foundations Event‑Driven Architecture: Designing asynchronous patterns where agent actions and enterprise services trigger downstream processes reliably. DevOps & MLOps: Establishing deployment, release, and lifecycle management approaches for software and AI‑enabled workflows. Cloud Infrastructure: Leveraging cloud‑native services such as serverless compute, queues, workflow engines, and managed databases to support scalable operations. Strategic & Leadership Skills Cross‑Disciplinary Leadership: Bridging Product, Engineering, Data Science, and Operations to drive aligned solution delivery. Complex Problem Solving: Identifying where agentic solutions provide meaningful business value and where conventional solutions are more appropriate. Influence & Communication: Explaining complex architectural decisions clearly to both technical and non‑technical stakeholders. Qualifications Required Bachelor’s degree in Computer Science, Engineering, Information Systems, Biomedical Engineering, or related field plus 7 years’ experience; OR Master’s degree plus 6 years’ experience; OR PhD with 2 years experience. Respective years of experience in software engineering, enterprise architecture, solution architecture, or application/platform delivery roles. Experience with AI/LLM‑enabled systems, agent orchestration patterns, RAG frameworks, or secure enterprise AI architecture. Demonstrated experience designing and implementing large‑scale enterprise application architectures. Strong experience with cloud‑native architecture, APIs, distributed systems, event‑driven design, and modern software delivery practices. Strong understanding of software development lifecycle (traditional and Agile methods), operational support models, and platform reliability principles. Experience working in regulated environments and applying security, compliance, and data governance requirements in architecture decisions. Proven ability to evaluate emerging technologies and turn them into practical, scalable business solutions. Excellent written, verbal, and stakeholder communication skills. Preferred Pharmaceutical, life sciences, R&D, or Safety domain experience. Deep knowledge of DevSecOps practices and CI/CD pipeline design, with hands‑on experience in Azure‑based development and deployment environments. Familiarity with modern enterprise technology platforms and tools, including Claude, Jira, Confluent, Docker, Git, JFrog, Tricentis (qTest/Tosca), ServiceNow, and Azure DevOps (ADO). Understanding of agentic development methodologies, including BMAD, with the ability to apply these approaches to scalable architecture and solution delivery. Benefits The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and the Company may ultimately pay more or less than the posted range. This range may be modified in the future. We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees. This job is eligible to participate in our long‑term incentive programs. Equal Opportunity Employer AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer / Veterans / Disabled. Disability and Accommodation Information US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: #J-18808-Ljbffr BioSpace