AI Engineer (Full Stack)

About

Driscoll’s is building an AI‑assisted RD capability to modernize how data, software, and decision support come together across breeding, genomics, lab, phenotyping, sensory, and agronomy workflows. This role sits within an emerging RD IT function embedded in Global RD and works closely with scientists, breeders, product leads, and Global IS to turn ambiguous scientific needs into production‑ready tools.

Responsibilities

• Build end‑to‑end solutions within the Driscoll’s RD IT ecosystem, including helping evolve the underlying infrastructure and delivery patterns.
• Design, build, and ship full‑stack applications that support RD workflows and decision‑making across breeding, lab, genomics, phenotyping, sensory, and agronomy use cases.
• Develop scalable services, APIs, workflow automations, and modern user interfaces using technologies such as React, TypeScript, Node.js/Express, Python, FastAPI, and relational databases.
• Integrate internal tools, vendor platforms, and off‑the‑shelf systems through APIs, data flows, authentication/authorization, and workflow automation.
• Build AI‑enabled applications such as grounded assistants, retrieval‑augmented experiences, workflow copilots, evaluation dashboards, and decision‑support interfaces.
• Own the full software lifecycle for what is built: requirements translation, prototyping, development, testing, deployment, monitoring, maintenance, support, and iterative improvement.
• Partner closely with scientists, breeders, analysts, and business stakeholders to translate ambiguous problems into usable, reliable products.
• Help embed AI into real workflows with appropriate traceability, security, auditability, and human oversight.
• Continuously evaluate how modern AI is changing software paradigms, and apply AI tools to improve engineering practices, team productivity, and solution quality.
• Contribute to governed AI delivery by ensuring applications are secure, observable, maintainable, and aligned with approved architecture and data patterns.
• Collaborate with the AI / MLOps Architect on model integration, serving patterns, evaluation flows, observability, and production operations.
• Contribute to architecture discussions, design reviews, backlog shaping, and solution tradeoff decisions.
• Communicate effectively, both verbally and in writing, with technical and non‑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.

Candidate Profile

• 5+ years of experience in software engineering, full‑stack development, or product engineering, including ownership of production systems.
• Expert‑level hands‑on experience across modern full‑stack technologies such as PostgreSQL/MySQL, Node.js/Express, TypeScript, React, Python, and FastAPI.
• Strong software engineering fundamentals, including API design, data modeling, testing strategies, observability, secure design, and maintainable architecture.
• Demonstrated ability to own problems end‑to‑end and ramp quickly across unfamiliar domains, technologies, and workflows.
• Experience building integrations across internal platforms, vendor systems, APIs, and workflow tooling.
• Experience shipping cloud‑based applications with CI/CD, source control, containerization, and modern developer tooling.
• Strong communication skills; able to explain tradeoffs, architecture, and design decisions to both technical and non‑technical stakeholders.
• Ability to work effectively 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

• Depth in data engineering, ML engineering, or applied data science.
• Experience integrating LLMs into products, including retrieval systems, prompt orchestration, evaluation workflows, fine‑tuning patterns, or human‑in‑the‑loop experiences.
• Experience building governed data or AI products with lineage, access controls, audit logs, quality gates, and approval flows.
• Professional experience with DevOps, cloud security, and infrastructure‑as‑code using tools such as GitHub, Docker, and AWS.
• Exposure to AI pipeline operations such as auto‑labeling,