Automation & Process Improvement Engineer

IntegriChain; Philadelphia (Philadelphia County); On-site; USD 110,000 - 140,000; Full time

Job summary

IntegriChain seeks a hands-on Automation Engineer to partner with operations teams, building automations and AI-powered integrations that reduce manual work and error rates. You’ll craft scripts, data pipelines, and API connections, embedding AI into practical workflows.

You will work on parsing unstructured data, improving processes, and ensuring reliable production systems with DevOps practices, code reviews, and thorough documentation.

Qualifications

• Hands-on experience automating processes in large operations.
• Strong programming skills, especially Python.
• Experience with AI tools and building AI-enabled workflows.
• Experience with systems integration and data pipelines.
• Ability to work embedded with operations teams.

Responsibilities

• Understand workflows with an operations team to identify automation opportunities.
• Develop scripts, scheduled jobs, APIs, and data pipelines to automate tasks.
• Simplify processes before automating by removing unnecessary steps.
• Build AI-augmented solutions like LLM-based text classification or agents.
• Measure improvements by tracking time saved and errors reduced.
• Maintain and monitor automations in production; provide runbooks.

Skills

Automation

Process improvement

Scripting

Python

AI integration

System integration

SQL databases

DevOps practices

Documentation

Monitoring

Tools

Cursor

Claude

Copilot

APIs

SQL

Job description

Mission

We're looking for someone who can sit with our operations teams and make their day-to-day work easier. You'll start with Managed Services, learn how they actually get things done, and then build the automations and integrations that take the repetitive, manual work off their plate. A lot of the job is scripting, connecting systems, and cleaning up messy processes, with AI tooling and concepts being a big part of it. We are looking for someone who has actually built with AI, both using it as a tool to work faster and building it into the solutions themselves, like putting LLM-powered steps or small agents right inside a workflow. We think the right person is smart, driven, and technically strong, with real experience automating and improving processes inside a large operation. You know automation, process improvement, and systems integration well, and you've got real hands-on experience with AI, both using it day to day and building it into working solutions. You care about results you can actually point to, like hours saved, fewer errors, and more work getting done without adding headcount.

Position Overview

This is a hands-on role for someone who likes digging into operational problems.

Here's what the work looks like:

• Embedded work: You'll be part of an operations team, starting with Managed Services. You'll learn their workflows and go after the highest-volume, most painful tasks first.
• Automation and integration: You'll write the scripts, jobs, and integrations that connect systems, move data around, and cut out manual steps.
• Process improvement: Before automating anything, you'll look for ways to simplify it. Sometimes the best fix is removing a step, not speeding it up.
• Building with AI: You'll build AI into the work itself, like an LLM that reads unstructured text or an agent that handles a multi-step task, and you'll use AI tools to build it all faster.
• Results: You'll measure where things stand today and track what changes, like time saved and errors avoided.

Key Responsibilities

• Process Improvement and Automation
• Spend time with an operations team to understand how the work really happens and where automation would make the biggest difference.
• Look for ways to simplify a process before automating it. Cut the steps that don't need to be there, then automate the rest.
• Build the scripts, scheduled jobs, API connections, and data pipelines that take manual work off people's plates.
• Connect the systems and data sources people rely on (internal tools, APIs, SQL databases, and other platforms) so information moves without someone copying it by hand.
• Measure each process before and after so you know what actually improved, then keep refining it.
• Keep what you build running. Monitor it, fix it when it breaks, and make sure people can count on it.
• Show team members how to use the new tools and workflows, and write things down so other teams can pick them up later.
• Building with AI and Engineering Practices
• Design and build AI into the solutions you ship, like an LLM that reads and classifies unstructured text, or an agent that carries out a multi-step task on its own.
• Use AI coding assistants such as Cursor, Claude, or Copilot to build faster, while keeping the quality of the code high.
• Know the limits. Be honest about where AI is reliable and where a straightforward, deterministic automation is the safer bet.
• Write clean, maintainable code, mostly in Python, and stick to good habits like version control, code review, and documentation.
• Work with DevOps and SRE on deployment, secrets, monitoring, and keeping your automations healthy in production.