Driscoll’s is building an AI-assisted R&D capability that depends on trusted data, governed delivery patterns, secure environments, and production-grade model operations. This role sits within an emerging R&D IT function embedded in Global R&D and partners closely with Global IS, scientists, product leads, and engineers to define how AI is safely and repeatedly deployed across breeding, genomics, lab, phenotyping, sensory, and agronomy workflows.
We are seeking an AI / MLOps Architect who can design and operationalize the backbone for governed AI at Driscoll’s. This role is responsible for the patterns, platforms, controls, and runtime operations that allow models and AI-enabled services to move from prototype to dependable production use. The ideal candidate combines strong architecture judgment with hands-on experience in MLOps, model serving, evaluation, observability, lineage, and secure deployment.
This is a hands-on architecture role for someone who enjoys building repeatable systems, reducing technical ambiguity, and creating a foundation that multiple R&D AI use cases can share. You will work closely with the AI Engineer, Product Owner, Full-Stack Engineer, data engineers, and domain partners to ensure AI solutions land on a common backbone rather than emerging as disconnected pilots.
Driscoll’s Information Services (IS) department is responsible for maintaining and developing digital services and solutions to support and enable the Driscoll’s business, growers, and customers. Global IS operates in a rapidly changing business environment and has embarked on a significant digital transformation journey.
The Global Information Services (IS) function operates through a global structure and is organized in departments by IT expertise. This role is located at our corporate headquarters in Watsonville, CA.
- 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.
- Domestic and international travel required up to 10%.
- 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.
- 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.
- Ability 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.
- Travel and after-hours support required.
Preferred Qualifications
- Experience with model registry, feature/data versioning, evaluation frameworks, experiment tracking, or deployment orchestration tools.
- Experience supporting LLM-based applications, retrieval systems, prompt orchestration, model routing, or assistant-style workflows in production.
- Experience with cloud-native architecture, especially AWS, and services supporting AI/ML deployment, data integration, and runtime operations.
- Experience with infrastructure-as-code, GitHub-based workflows, Docker, and environment automation.
- Familiarity with data lineage, cataloging, semantic layers, and governed access patterns for AI-enabled applications.
- Experience partnering with product managers, application engineers, and data engineers in cross-functional delivery squads.
- Experience in both in-house development solutions and implementation of vendor-delivered applications preferred.
- Familiarity with scientific/R&D datasets, high-throughput lab systems, genomics, phenotyping, breeding, or ag-biotech environments.
- Prior experience defining reference architectures and evangelizing standards across multiple teams or business units.
- A valid passport and the ability to travel internationally without restrictions.
Compensation and Benefits
The following information is provided in good faith as a general description of the salary range and benefits for the position posted. The actual compensation offered to the successful candidate is dependent upon experience, skills, education, work location, internal pay equity, and other objective job-related factors.
Salary Range estimated for the AI/MLOps Architect - R&D IT: $132,410.00/year to $170,000.00/year
Driscoll’s is committed to a culture of care and offers an attractive benefits package that includes comprehensive medical, dental, and vision coverage, life insurance, and disability coverage for positions working more than 30 hours per week. Other benefits include: 401(k) with employer match, profit-sharing participation, paid sick time, paid vacation, paid personal and family care leave, and a free Employee Assistance Program (EAP). More detailed information regarding the benefits package, will be shared during the application process.
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