AI Development & Support Engineer

Starfish Storage Corporation; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Starfish Storage manages petabytes of unstructured data for national labs, research institutions, fintech, and pharma, and is building an AI-native layer with MCP servers, agentic workflows, and local LLM deployments. Researchers and engineers will interact with storage infrastructure through tools that enable autonomous data actions and governance.

In this role you will develop MCP servers, deploy and tune local LLMs, create production-grade agents for real customers, and run in constrained

Qualifications

• Solid grasp of agent architectures, prompt engineering, and MCP or tool-use patterns.
• Hands-on experience with local/open-source LLMs: you've run them, tuned them, deployed them.
• Comfortable working in Linux environments and debugging at the command line.
• Strong communicator: you can explain what's broken and why to both engineers and non-engineers.
• This is not a role for someone who has tried out some youtube tutorial videos on their own. You need to have some real experience implementing this.

Responsibilities

• Build and expand MCP servers that expose Starfish's data management capabilities to LLM-powered workflows.
• Develop intelligent agents that can reason over storage metadata at scale — including local LLM deployment and configuration using vLLM, Ollama, or similar.
• Create workflow automation and chat-based interfaces for querying and acting on Starfish data.
• Build proof-of-concept implementations and turn them into production-grade features.
• Support customers through installation, configuration, and troubleshooting of AI features and integrations.
• Get on calls with technical teams, understand their use cases, and translate requirements into working agents.

Skills

Agent architectures

Prompt engineering

Local/open-source LLMs

Linux

Command-line debugging

Python development

RAG pipelines

REST APIs

Docker

Storage systems / HPC

Communication with engineers

Tools

vLLM

Ollama

Vector databases

Job description

Location

Your home office or our Waltham, MA HQ or hybrid

Qualifications

Hard Requirements

• Solid grasp of agent architectures, prompt engineering, and MCP or tool-use patterns
• Hands-on experience with local/open-source LLMs: you've run them, tuned them, deployed them
• Comfortable working in Linux environments and debugging at the command line
• Strong communicator: you can explain what's broken and why to both engineers and non-engineers
• This is not a role for someone who has tried out some youtube tutorial videos on their own. You need to have some real experience implementing this.

Helpful Background

• 2+ years Python development — real shipped and used code
• Experience with RAG pipelines or vector databases
• Familiarity with REST APIs and integration patterns
• Docker comfort
• Background in storage systems or HPC environments (this helps more than you think)

Working Style

• You can context-switch between deep development work and supporting a customer mid-debug
• You're comfortable being the person who figures things out when there's no playbook yet

About Starfish Storage

Starfish Storage is an established company that maintains a startup culture mindset. We cater to a diverse client base, serving industries such as life sciences, artificial intelligence, and research computing. Our technology is critical to modern organizations, helping customers organize, manage, and move the tens of billions of files central to their operations.

Most AI work right now is happening in the cloud, with managed APIs, and relatively clean data. Some of our customers can't do that: they're operating in classified or regulated environments with filesystem metadata spanning billions of files. The AI problems here are genuinely hard, the deployments are genuinely constrained, and the work actually matters to science and research.

If you want AI work that's genuinely constrained, genuinely consequential, and can't phone home to OpenAI — this is it.

Job Overview

Starfish manages petabytes of unstructured data for national labs, research institutions, fintech, and pharma, the kind of data that doesn't fit neatly into databases or cloud buckets. We're building an AI-native layer on top of that: MCP servers, agentic workflows, local LLM deployments, and tools that let researchers and engineers actually talk to their storage infrastructure. We're tackling hard questions too: should an agent be permitted to move 2PB of data autonomously? How do you govern that?

In this role, you'll build and ship MCP servers, deploy and tune local LLMs, write custom agents for real customers with real petabyte-scale problems, and get them running in environments where "just use the API" isn't an option. You'll also support customers when things don't work: which means you need to actually understand what you've built.

Responsibilities and Duties include:

MCP & Agent Development

• Build and expand MCP servers that expose Starfish's data management capabilities to LLM-powered workflows
• Develop intelligent agents that can reason over storage metadata at scale — including local LLM deployment and configuration using vLLM, Ollama, or similar
• Create workflow automation and chat-based interfaces for querying and acting on Starfish data
• Build proof-of-concept implementations and turn them into production-grade features

Customer-Facing Technical Work

• Support customers through installation, configuration, and troubleshooting of AI features and integrations
• Get on calls with technical teams, understand their use cases, and translate requirements into working agents