AI Platform Engineer

United States Cold Storage, Inc.•Conshohocken, PA

4d•$140,000 - $150,000•Hybrid

About The Position

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

Requirements

Hands-on software engineering experience building applications, APIs, integrations, automation, or data-driven solutions using Python, Java, JavaScript/TypeScript, or comparable languages, with at least 4 years of professional software development experience and demonstrated experience building automation solutions.
Applied AI experience. At least 3 years of hands-on experience developing and delivering Generative AI solutions, with familiarity in 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.

Responsibilities

Design and build internal copilots, agents, RAG applications, and workflow automations for engineering and IT use cases.
Ingest, structure, chunk, embed, retrieve, and govern content from application code, documentation, runbooks, standards, tickets, and other approved sources.
Develop tool-enabled agents and MCP servers that securely connect AI assistants to enterprise systems, APIs, repositories, and approved data sources.
Build capabilities for code understanding, code review, test generation, documentation, modernization, incident analysis, ticket resolution, and repetitive delivery tasks.
Evaluate models, frameworks, prompts, retrieval approaches, and AI-assisted development tools; document findings and recommend fit-for-purpose patterns.
Create test datasets, automated evaluations, grounding checks, human-feedback loops, observability, and quality gates to improve accuracy and reliability.
Partner with architecture, cloud, security, and platform teams to implement identity, access controls, monitoring, cost controls, auditability, deployment pipelines, and support documentation.