Senior AI Engineer

Welcome to Gordon Food Service! We are excited that you are thinking about opportunities with us, and we have an amazing story to share. See below for a quick glance of who we are and the impact you could have on the food service industry. There's a seat at our table for you...

Do you believe that multi-agent and orchestrated AI systems can solve critical business challenges and redefine how teams operate?

Are you excited about architecting complex solutions that leverage the Google Vertex AI Platform, AgentSpace, and other advanced technologies?

Do you enjoy mentoring, guiding, and shaping the future of AI within an organization?

Is collaborating with AI to create secure, scalable, and efficient code across modern languages second nature to you?

If you answered yes, we may need to meet.

We’re looking for a Senior AI Engineer on our Emerging Technology team to lead the charge in enabling multi-agent systems across the organization. In this role, you will drive the technical implementation, focusing on orchestrating agent-based architectures, robust data pipelines, and MLOps best practices. You’ll pair your deep technical expertise with hands-on leadership, ensuring each solution is production-ready, scalable, and capable of generating real impact.

Essential Functions

Advanced AI Architecture & Model Development

• Architect and develop complex agentic systems and machine learning models (e.g., deep learning, NLP, computer vision) using frameworks like TensorFlow, PyTorch, or other relevant libraries—employing modern programming languages (e.g., Python, JavaScript/TypeScript, Java/Kotlin) best suited for the solution.

• Integrate AI-powered coding and prototyping tools where beneficial to expedite development and enhance code quality.

• Leverage Google Vertex AI, AgentSpace, and other cutting-edge platforms to orchestrate multi-agent systems that meet business objectives.

• Ensure best practices in hyperparameter tuning, model optimization, and experimentation for peak performance.

Data Pipeline Engineering & Large-Scale Deployment

• Lead the design and implementation of scalable data pipelines for ingestion, cleansing, and transformation.

• Champion MLOps best practices, using Docker, Kubernetes, and cloud services to orchestrate reliable CI/CD pipelines for model deployment. Partner with AI-driven testing and deployment automation solutions to reduce errors and shorten iteration cycles.

• Monitor model performance in real time, troubleshooting issues and optimizing for stability and speed.

Technical Leadership & Mentorship

• Provide guidance and mentorship to junior AI Engineers, reviewing code, promoting engineering excellence, and fostering a continuous learning culture.

• Partner with Product Management and other cross-functional stakeholders to prioritize AI initiatives, ensuring alignment with business goals and user needs.

• Coordinate with fellow AI Engineers on solution design, architectural decisions, and proof-of-concept projects, leveraging each other’s expertise to drive innovation.

• Collaborate cross-functionally to align on AI-driven initiatives, translating business needs into technical requirements.

• Educate stakeholders on AI’s capabilities, limitations, and best use cases, driving data-informed decision-making.

• Promote best practices for employing AI-based coding solutions, ensuring the team leverages automated insights and suggestions effectively to accelerate development.

Performance Monitoring & Ongoing Optimization

• Regularly assess agent & model performance metrics (latency, accuracy, precision, etc.) to ensure solutions remain at top performance.

• Refine algorithms, update pipelines, and adopt new tools to keep up with changing data and evolving business needs.

• Utilize AI-driven analytics for real-time feedback on solution performance, proactively refining code and processes.

• Experiment with additional cloud-based AI services like Dialogflow, Gemini, and BigQuery ML to enhance solution capabilities.

Strategic Input & Governance

• Contribute to solution architecture reviews, balancing factors like scalability, performance, and cost-effectiveness.

• Maintain awareness of AI ethics, data governance, and co