Software Engineer III
Software Engineer - III
About the Role
GHX is building a next-generation Intelligent Process Automation (IPA) platform powered by LLMs and AI-native document understanding. We extract structured data from complex healthcare procurement documents — Purchase Orders, invoices, contracts — at scale across cloud environments. As a member of the IPA engineering team, you will bridge strong software engineering with applied AI. You will design and ship Python services, integrate LLM APIs, build AI agents, create skills and validate the output of LLMs to build document extraction pipelines, and own evaluation infrastructure that ensures production quality. This is not a research role — it is a hands-on engineering role where AI fluency amplifies solid software craft.
Core Responsibilities
Python Development & Automation
Build and maintain Python-based automation services and IPA workflows
Develop platform-agnostic solutions supporting future migration across automation tooling
Build reusable libraries, frameworks, and components for automation projects
Integrate automation solutions with REST APIs and enterprise applications
Deploy and monitor automation bots on AWS (Lambda, ECS, SQS, S3)
AI & LLM Integration
Integrate LLM APIs (like Anthropic Claude, OpenAI, Azure AI) into production pipelines
Design classification and extraction prompts for diverse document types (POs, invoices, contracts)
Write prompts that function as formal specifications — unambiguous, edge-case-aware
Build and iterate few-shot, chain-of-thought, and structured output templates
Own prompt library versioning, rollback strategy, and prompt lifecycle management
AI & LLM Integration
Integrate LLM APIs (like Anthropic Claude, OpenAI, Azure AI) into production pipelines
Design classification and extraction prompts for diverse document types (POs, invoices, contracts)
Write prompts that function as formal specifications — unambiguous, edge-case-aware
Build and iterate few-shot, chain-of-thought, and structured output templates
Own prompt library versioning, rollback strategy, and prompt lifecycle management
Document Processing & IDP
Implement OCR/IDP workflows to extract and validate structured and unstructured data
Build confidence scoring frameworks for extracted fields with multi-factor validation
Design and curate ground truth datasets for classification and extraction tasks
Build automated evaluation pipelines tracking precision, recall, and field-level accuracy
Validate model outputs against intent — catch what is technically correct but conceptually wrong
Software Engineering
Design and build production-grade APIs and services around LLM capabilities
Apply Clean Architecture or equivalent — design for testability and maintainability
Contribute to CI/CD pipelines, observability tooling, and deployment processes
Conduct code reviews, establish coding standards, and mentor junior engineers
Troubleshoot and optimize existing automation workflows and Python services
Collaboration & Communication
Work closely with business analysts and stakeholders to understand process requirements
Participate in Agile ceremonies — sprint planning, retrospectives, and estimation
Communicate system constraints in product language; translate product needs into system boundaries
Surface quality metrics and model behaviour clearly to non-technical stakeholders
Required Qualifications
Bachelor's degree in Computer Science, Engineering, or related field
5–9 years of software engineering experience, with at least 4 years in Python
Strong Python skills — Pandas, NumPy, Flask/Django, async programming
Hands-on experience with LLM APIs (like Anthropic Claude, OpenAI, or Azure AI)
Prompt engineering experience — structured outputs, few-shot, chain-of-thought
2+ years of experience with SQL and NoSQL databases
Experience deploying applications on AWS — Lambda, SQS, S3, ECS
Knowledge of Docker, Git, and CI/CD pipelines (Jenkins or equivalent)
Familiarity with REST API design and tools like Swagger and Postman
Understanding of OCR/IDP tools — AWS Textract, UiPath Document Understanding, or ABBYY
Desired Qualifications
Experience with AI agent orchestration frameworks — LangChain, LlamaIndex, or MCP
Familiarity with evaluation frameworks for LLM outputs (precision, recall, F1 at field level)
NLP / text classification background
UiPath Associate or Advanced RPA Developer certification (or equivalent)
Exposure to Clean Architecture, DDD, or equivalent design patterns
Understanding of Agile/Scrum methodologies
What Good Looks Like
Prompt as specification — treats prompts like contracts: precise, unambiguous, edge-case-aware from the start
Verification instinct — reads AI output critically; catches the subtly wrong answer before it becomes a data quality incident
Evaluation-first mindset — doesn't trust a prompt in production without a test suite; builds measurement as part of the feature
Engineering rigour — applies the same discipline to Python services as to LLM pipelines; Clean Architecture is the default, not the exception
Communication range — can speak to a non-technical ops manager and a distributed system design at the same level of precision