Automation & Process Improvement Engineer

IntegriChain • Philadelphia, PA

2d • Hybrid

About The Position

IntegriChain is the data and application backbone for market access departments of Life Sciences manufacturers. We deliver the data, the applications, and the business process infrastructure for patient access and therapy commercialization. More than 250 manufacturers rely on our ICyte Platform to orchestrate their commercial and government payer contracting, patient services, and distribution channels. ICyte is the first and only platform that unites the financial, operational, and commercial data sets required to support therapy access in the era of specialty and precision medicine. With ICyte, Life Sciences innovators can digitalize their market access operations, freeing up resources to focus on more data-driven decision support. With ICyte, Life Sciences innovators are digitalizing labor-intensive processes – freeing up their best talent to identify and resolve coverage and availability hurdles and to manage pricing and forecasting complexity.
This role offers flexibility, but candidates must reside in Pennsylvania, New Jersey, or New York and be within a reasonable travel distance of our Philadelphia office, as regular in-person collaboration is required.
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.
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.

Requirements

• At least 4 years automating and improving processes, or building integrations and software, ideally for a large or high-volume operation.
• A track record of automating real work and being able to show what it saved, whether that's time, errors, or extra capacity.
• Solid Python (or something similar) for scripting and building integrations.
• Real experience connecting systems through APIs and working with SQL and relational data.
• A knack for process improvement. You can look at how something works, spot what's slowing it down, and redesign it instead of just automating the mess.
• Real hands-on experience building with AI, not just using it. You've put LLMs or agents into something real, and you know how to use AI tools to speed up your own work.
• A working understanding of common LLM patterns, like prompting, tool or function calling, and wiring an agent into a workflow.
• Comfort working closely with non-engineering teams. You listen well, turn their needs into solutions, and explain technical things without the jargon.
• Self-directed and steady when priorities shift, and things move fast.
• Good at troubleshooting and debugging, including for things running live in production.

Nice To Haves

• Experience with workflow automation, RPA, or job orchestration tools.
• Experience working alongside operations or managed services teams to roll out technology.
• Hands-on work with LLM APIs, tool or function calling, or agent-style workflows.
• Experience deploying apps or services on AWS (containers, serverless, secrets, IAM, monitoring).
• Experience with data platforms and analytics tools like Snowflake, semantic models, or natural language to SQL.
• Experience with FastAPI, Streamlit, React, or similar frameworks.
• Background in life sciences, healthcare, or pharma commercialization data such as MDM, patient, or channel data

Responsibilities

• 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.
• 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.

Benefits

• Excellent and affordable medical benefits + non-medical perks including Student Loan Reimbursement, Flexible Paid Time Off and Paid Parental Leave
• 401(k) Plan with a Company Match to prepare for your future
• Robust Learning & Development opportunities including over 700+ development courses free to all employees

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed