AI Platform Engineer (USA - Remote)

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 at 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 build 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 GitHub 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 qualifications:

• Hands-on software engineering experience building applications, APIs, integrations, automation, or data-driven solutions using Python, Java, JavaScript/TypeScript, or comparable languages.
• Applied AI experience. Familiarity with large language models, prompt engineering, RAG, embeddings, vector search, AI agents, or model APIs through professional work or substantive projects.
• Cloud and platform fundamentals. Experience with cloud services, containers, source control, CI/CD, secrets management, identity, logging, and monitoring; Azure experience is preferred.
• Data and integration skills. Ability to work with REST APIs, JSON, SQL, relational databases, document stores, search technologies, and structured or unstructured enterprise content.
• Quality mindset. Experience writing maintainable code, automated tests, technical documentation, and evaluation criteria, with attention to security, privacy, accuracy, and operational reliability.
• Problem-solving and experimentation. Ability to compare technical approaches, learn new tools quickly, troubleshoot independently, and convert experimental findings into reusable engineering patterns.
• Collaborative communication. Able to work with engineers, architects, security partners, support teams, and business stakeholders to clarify problems, demonstrate solutions, and incorporate feedback.
• Builder mindset. You are curious, pragmatic, and motivated by shipped capabilities and measurable improvements rather than technology for its own sake.

Technical Environment

• Azure-first cloud environment, including identity, networking, application services, data services, monitoring, and secure infrastructure automation
• Generative AI and agentic development using Azure AI services, model APIs, RAG patterns, vector search, MCP, agent frameworks, and AI-assisted development tools
• Languages and engineering tools such as Python, Java, JavaScript/TypeScript, REST APIs, GitHub, CI/CD pipelines, containers, automated testing, and observability
• Enterprise knowledge sources including application code, architecture documentation, runbooks, engineering standards, service tickets, and collaboration platforms
• Application and data landscape including Java, React, Postgres, APIs, integrations, and event-driven or service-oriented architectures
• Secure and measurable AI delivery, including evaluations, human feedback, access controls, auditability, model and cloud consumption, and operational support

Why This Role Is Different

• You will build AI capabilities that solve real engineering and operational problems, not isolated demonstrations
• You will gain exposure to a broad technology landscape spanning modern platforms, legacy systems, data, integrations, and physical operations
• Your work will be evaluated through adoption, time saved, quality, reliability, and measurable productivity improvement
• You will help establish reusable AI engineering patterns and influence how technology adopts AI safely at scale