AI Platform Engineer

• Pay or shift range: $105,000 USD to $130,000 USD
  The estimated range is the budgeted amount for this position. Final offers are based on various factors, including skill set, experience, location, qualifications and other job-related reasons.

Build practical AI solutions that improve how our technology organization delivers, supports, and operates systems.

Who We Are:

US Cold owns and operates one of the most complex temperature-controlled logistics networks in North America. Every day, our systems coordinate the storage and movement of food on a national scale across a network of state-of-the-art distribution centers, including multiple highly automated warehouse facilities.

We continue to advance our core warehouse and logistics platforms. Our current focus is on modular, event-driven, API-first, and cloud architecture. We continue to enhance reliability and accelerate engineering productivity by strengthening our SRE and AI practices. This is a large investment in innovation to continue to drive operational excellence at our facilities.

If you want to help us accelerate the building of durable systems that operate in the physical world at scale, this is that opportunity.

The Role:

You will be a hands-on engineer in the AI Platform function, helping turn high-value ideas into secure, measurable solutions that improve engineering productivity and technology operations. Working closely with the Senior Manager of Engineering Productivity and teams across Technology, you will prototype, build, test, and operationalize AI-assisted and agentic capabilities.

Your work will span the delivery lifecycle—from requirements and design through software development, testing, deployment, and support. You will create reusable AI services, enterprise knowledge solutions, Retrieval-Augmented Generation (RAG) systems, agents, MCP servers, workflow automations, and integrations with tools such as Git Hub Copilot, Cursor, Claude Code, Azure AI services, and future AI platforms.

This is a hands-on engineering role rather than a research-only position. You will experiment quickly, but the goal is to convert successful prototypes into reliable, governed capabilities that teams can use in their day-to-day work.

You will report to the Senior Manager of Engineering Productivity and collaborate with software, data, cloud, infrastructure, security, service management, and business teams. Success will be measured through adoption, time saved, improved quality, faster delivery, and reduced operational toil.

What You’ll Do:

• AI Solution Development — design and build internal copilots, agents, RAG applications, and workflow automations for engineering and IT use cases.
• Enterprise Knowledge Platforms — ingest, structure, chunk, embed, retrieve, and govern content from application code, documentation, runbooks, standards, tickets, and other approved sources.
• Agent and MCP Engineering — develop tool-enabled agents and MCP servers that securely connect AI assistants to enterprise systems, APIs, repositories, and approved data sources.
• Engineering Productivity Automation — build capabilities for code understanding, code review, test generation, documentation, modernization, incident analysis, ticket resolution, and repetitive delivery tasks.
• Rapid Prototyping and Experimentation — evaluate models, frameworks, prompts, retrieval approaches, and AI-assisted development tools; document findings and recommend fit-for-purpose patterns.
• Evaluation and Quality — create test datasets, automated evaluations, grounding checks, human-feedback loops, observability, and quality gates to improve accuracy and reliability.
• Production Readiness — partner with architecture, cloud, security, and platform teams to implement identity, access controls, monitoring, cost controls, auditability, deployment pipelines, and support documentation.

What We Are Looking For:

You are a practical software engineer who enjoys learning emerging AI technologies and turning ambiguous problems into working solutions. You can build beyond a demo, explain technical choices clearly, and collaborate with others to safely move useful capabilities toward production.

Core…