AI/MLOps Architect - R&D IT (Onsite)

Responsibilities

• Define and evolve the reference architecture for AI and model operations across the R&D IT ecosystem.
• Establish repeatable patterns for model packaging, deployment, serving, evaluation, monitoring, retraining, rollback, and lifecycle governance.
• Design and implement the technical backbone for governed AI, including model registry patterns, evaluation flows, observability, lineage, auditability, and access controls.
• Partner with R&D IT, Global IS, and data/platform teams to ensure AI solutions land on approved architecture, environments, and data pathways rather than separate, ungoverned stacks.
• Define minimum standards for production AI services, including environment separation, release controls, security, performance, logging, approvals, and recovery procedures.
• Develop and standardize patterns for integrating models and AI services into applications, APIs, workflow tools, and enterprise platforms.
• Design model-serving and inference patterns for different use cases, including batch, near-real-time, and interactive assistant workflows.
• Establish practical evaluation approaches for AI-enabled systems, including offline testing, human-in-the-loop review, regression checks, drift monitoring, and quality gates.
• Drive technical decisions around observability, cost/performance tradeoffs, model telemetry, and operational supportability.
• Partner with the AI Engineer and Full-Stack Engineer to ensure product experiences are backed by reliable, scalable, and measurable AI services.
• Work with product and domain stakeholders to translate scientific workflows into durable operational patterns and platform requirements.
• Contribute to roadmap planning, architecture reviews, vendor assessment, backlog shaping, and implementation sequencing.
• Mentor engineers on deployment patterns, infrastructure tradeoffs, service design, evaluation, and operational excellence.
• Communicate effectively, both verbally and in writing, with business and technical teams.
• Represent Driscoll’s in an ethical and professional manner during all interactions with growers, co-workers, suppliers, customers, and the business community at large.
• Ensure the security of Driscoll’s confidential and proprietary information and materials.

Qualifications

• 5+ years of experience in machine learning engineering, platform engineering, MLOps, cloud architecture, or adjacent technical roles supporting production AI/ML systems.
• Hands-on experience designing or operating model deployment and serving patterns in cloud environments.
• Strong experience with modern software and platform engineering practices, including CI/CD, containers, service reliability, versioning, observability, and secure deployment.
• Experience with Python and API/service integration patterns; working knowledge of SQL and data access patterns.
• Practical experience with model lifecycle operations, including deployment, monitoring, retraining triggers, evaluation, rollback, and incident response.
• Experience designing systems with traceability, auditability, access controls, and quality gates.
• Strong systems thinking and architecture judgment; able to create standards that are pragmatic, repeatable, and usable by engineering teams.
• Strong communication skills; able to explain architecture, tradeoffs, and risks to both technical and non-technical stakeholders.
• Able to thrive in a dynamic, cross-functional environment while living Driscoll’s values of passion, humility, and trustworthiness.
• Strong experience with Microsoft product suite, including Visio, Excel, PowerPoint, Word, Teams, and SharePoint required.
• Ability to travel domestically and internationally required.