- Bachelor's, Master's degree preferably in computer science, engineering, information systems, data science, or a related field. A combination of education, training, and experience may be considered in lieu of a degree.
- Minimum of 12 years of progressive experience in software engineering, solution architecture, platform engineering, data engineering, machine learning engineering, or a related technical discipline.
- Experience designing, building, or operating production machine learning, generative AI, LLM, agentic, or other AI-enabled systems.
- Experience defining architecture and operating models for MLOps, LLMOps, AI platform engineering, or comparable production AI capabilities.
- Demonstrated experience with at least three of the following: model and prompt lifecycle management, RAG, embeddings and vector search, agent orchestration, model gateways, evaluation frameworks, guardrails, structured outputs, human-in-the-loop workflows, or AI safety controls.
- Experience designing observability for AI/ML systems, including application health, model quality, latency, cost, usage, drift, safety, and regression monitoring.
- Strong understanding of software architecture, APIs, microservices, event-driven systems, databases, data flows, cloud platforms, security, identity, and enterprise integrations.
- Hands-on experience with Python and/or TypeScript/JavaScript and familiarity with modern AI/ML frameworks, orchestration tools, APIs, and cloud services.
- Demonstrated ability to lead architecture reviews and influence decisions across multiple teams and stakeholder communities.
- Ability to distinguish product development responsibilities from operational responsibilities and establish clear ownership boundaries.
- Excellent communication skills, written, verbal, and presentation, for both technical and non-technical audiences.
Preferred qualifications:
- Experience working with containerized workloads, CI/CD, infrastructure as code, automated testing, and platform automation preferred.
- Experience translating complex business and technical requirements into architecture decisions, solution designs, implementation guidance, and operational standards.
- Experience creating architecture diagrams, interface specifications, dependency maps, architecture decision records, operational runbooks, and technical documentation.
- Strong understanding of data quality, data governance, privacy, security, access control, auditability, and responsible AI considerations.
- Experience supporting production incidents, root-cause analysis, technical debt planning, system lifecycle decisions, and continuous improvement.
- Strong collaboration skills and the ability to build trust with engineering, product, data, security, infrastructure, business, and support partners.
- Demonstrated ability to work effectively in an environment with ambiguity, evolving requirements, multiple priorities, and rapid delivery expectations.
- Experience using AI-assisted development, architecture, documentation, or testing tools to improve speed and quality while applying appropriate judgments and controls.
Certain states and localities require employers to post a reasonable estimate of the salary range. A reasonable estimate of the current base pay range for this position is $124,800 to $187,200 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that are not included in the base pay.
The well-being of WWT employees is essential. When it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full-time employees:
- Health and Wellbeing: Health (Medical & Prescription), Dental, and Vision Care, Onsite Health Centers (MO & IL), Employee Assistance Program, Wellness program
- Financial Benefits: Competitive Pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Flexible Spending Accounts, Tuition Reimbursement
- Paid Time Off: PTO & Holidays, Parental Leave, Medical Leave, Military Leave, Bereavement, Day of Caring
- Additional Perks: Family Planning Benefits, Nursing Mothers Benefits, Voluntary Legal, Voluntary Supplemental Accident/Illness/Hospital, Voluntary ID Theft, Pet Insurance, Employee Discount Program
Note: This is not an all-encompassing list and should not be used as a complete description of the plan's benefits. For more information, see our US Benefits Website
We are looking for a senior, technically minded architect and problem solver who can define how AI, machine learning, and large language model capabilities are designed, deployed, operated, and improved in production.
This role will support the Lead to Quote and Win to Deliver platforms and its AI-enabled solutioning capabilities, including agentic workflows, LLM-backed services, retrieval-augmented generation, model integrations, and the operational practices required to run them reliably. The role blends architecture, hands-on technical leadership, operational readiness, and strong collaboration with engineering, product, data, security, infrastructure, and IT support teams.
You will establish practical architecture standards for ML/LLM Ops while staying close to the technology. You will balance your time between hands-on development, building reference implementations, reviewing code and prompts, and prototyping evaluation and observability patterns. You will help teams make sound decisions about model and agent lifecycle, prompt and configuration management, observability, evaluation, safety, cost, security, release management, incident response, and support ownership.
This role is distinct from a traditional application architect and from a standard infrastructure operations role. You will help define the technical and functional boundary between AI feature delivery and AI/ML operations, ensuring that capabilities can move from development into production support with clear ownership, documented controls, and measurable operational health. You will own the ML/LLM Ops architecture standards and operational readiness criteria and will partner with the Lead to Quote and Win to Deliver Technical/Solutions Architects and delivery teams who retain ownership of the overall application, integration, and data architecture.
You will operate in a dynamic, fast-paced environment supporting a growing product and delivery organization. Success in this position will require close collaboration with leadership, business stakeholders, architects, engineers, data practitioners, security partners, vendors, and service providers to ensure that AI-enabled solutions are secure, supportable, scalable, and aligned with business outcomes.
Responsibilities:
- Optimize and streamline data pipelines for efficient data preprocessing, feature engineering, and model training.
- Design model, agent, prompt, configuration, and evaluation lifecycle practices, including versioning, promotion, rollback, change control, and retirement.
- Establish observability standards for availability, latency, errors, throughput, quality, safety, cost, usage, drift, and regression signals.
- Define operational readiness criteria, support documentation, runbooks, monitoring, alerting, ownership, and escalation paths for AI/ML capabilities.
- Partner with engineering and product teams to identify operational requirements early in the design and delivery lifecycle.
- Provide technical direction for retrieval-augmented generation, embeddings, vector search, model gateways, prompt orchestration, agent tools, structured outputs, and human-in-the-loop workflows.
- Guide architecture decisions involving data quality, data access, security, privacy, permissions, auditability, and responsible use of AI
- Establish practical evaluation and testing approaches for model and agent behavior, including functional, regression, safety, quality, and performance testing.
- Partner with the Technical/Solutions Architects for Lead to Quote to ensure AI/ML capabilities fit coherently within the broader application, integration, data, and enterprise architecture.
- Work with the AI Agents, Core Platform, Data, QA, Security, Support, and IT Architecture teams to resolve cross-functional technical risks and dependencies.
- Lead architecture reviews, document architecture decisions, and communicate trade-offs to both technical and non-technical audiences.
- Create and maintain architecture documentation, operational models, dependency maps, and technical standards.
- Support knowledge transfer to IT delivery and support teams so that operational ownership is demonstrated, not simply documented.
- Identify technical debt, lifecycle risks, tooling gaps, and opportunities to standardize reusable AI/ML capabilities across WWT.
- Help shape the long-term ML/LLM Ops team model, including required skills, processes, tooling, service ownership, and coverage.
- Promote an AI-first mindset by using modern AI tools thoughtfully to improve architecture analysis, documentation, testing, automation, and engineering outcomes.
- Provide mentoring and technical guidance to architects, engineers, and delivery team members without relying solely on formal reporting authority.
- Escalate and remove technical impediments that prevent teams from delivering safe, supportable, and reliable AI-enabled solutions.
- Prepare and deliver informal and formal presentations to architecture teams, IT leadership, delivery teams, business partners, and stakeholders.
- Effectively cope with ambiguity and change while creating enough structure for teams to make consistent technical decisions.
