[Hiring] Python Developer @Highmark Health

Role Description

• Design, develop, and maintain Python-based backend services and APIs for AI-enabled applications, with strong emphasis on clean, modular, production-ready code.
• Build and integrate Generative AI workflows using LLMs, RAG pipelines, prompt orchestration, and structured-output processing.
• Develop FastAPI-based REST endpoints to support file processing, pipeline execution, asynchronous workflows, and integration with external enterprise applications.
• Implement retrieval workflows using GCP services such as Vertex AI, Vertex AI Search, Cloud Storage, BigQuery, Pub/Sub, Secret Manager, and Cloud Run.
• Develop data-processing logic for structured and unstructured inputs, including document parsing, metadata generation, validation, transformation, and downstream persistence.
• Support integration testing, debugging, deployment, logging, monitoring, and troubleshooting of AI applications across development and QA environments.
• Collaborate with solution architects, cloud infrastructure teams, product owners, and business stakeholders to translate requirements into working AI features and scalable backend components.
• Contribute to prompt engineering, output validation, error handling, retry logic, and guardrails to improve reliability of AI pipeline responses.
• Maintain technical documentation, API contracts, configuration files, deployment artifacts, and reusable engineering components.

Qualifications

• 4–7 years of software development experience, with strong hands-on experience in Python backend development.
• At least 1–2 years of practical experience with Generative AI, LLM-based applications, or RAG solutions.
• Experience building REST APIs using FastAPI, Flask, or similar Python frameworks, preferably FastAPI.
• Hands-on experience working with Google Cloud Platform, especially Cloud Run, Cloud Storage, Vertex AI, BigQuery, Pub/Sub, IAM, and Secret Manager.
• Experience integrating AI/ML services into enterprise applications using APIs, authentication, asynchronous processing, and cloud-native services.
• Experience processing structured and unstructured data formats such as JSON, JSONL, Excel, CSV, PDF, Word documents, and text.
• Experience with source control, CI/CD, containerization, and deployment workflows using Git, Docker, GitLab/GitHub, and cloud deployment pipelines.
• Exposure to debugging distributed or cloud-hosted applications using logs, monitoring, request tracing, and error analysis.
• Experience working in Agile delivery teams and collaborating with cross-functional stakeholders.

Requirements

• Python – strong programming fundamentals, object-oriented design, modular coding, exception handling, testing, and package management.
• Generative AI / LLMs – prompt engineering, structured outputs, model invocation, response validation, token usage, and basic evaluation concepts.
• RAG – document ingestion, chunking, embeddings, vector retrieval, metadata filtering, reranking, and grounding.
• GCP – practical experience with Vertex AI, Vertex AI Search, Cloud Run, GCS, BigQuery, Pub/Sub, Secret Manager, IAM, and Cloud Logging.
• FastAPI / REST APIs – endpoint development, request/response models, authentication, async processing, status handling, and API integration.
• Data Processing – pandas, openpyxl, JSON/JSONL handling, document processing, validation, and transformation.
• Cloud-Native Development – Docker, service accounts, environment variables, secrets, CI/CD, deployment, and runtime troubleshooting.
• Git – branching, pull requests, merge conflict resolution, version control, and collaborative development.
• Testing & Debugging – unit testing, API testing, integration testing, Postman/cURL, log analysis, and production issue troubleshooting.

Preferred / Good-to-Have Skills

• Experience with Agentic AI frameworks or multi-step AI orchestration.
• Familiarity with Vertex AI Vector Search, Graph RAG, or knowledge graph concepts.
• Experience with Pub/Sub-based asynchronous processing and event-driven architectures.
• Understanding of OAuth, service-to-service authentication, IAM, signed URLs, and secure API integration.
• Exposure to React or frontend integration is useful but not mandatory.
• Familiarity with AI evaluation metrics such as groundedness, relevance, completeness, coherence, and confidence scoring.
• Healthcare-domain exposure is beneficial, especially where the solution involves enterprise workflow, requirements, or document intelligence.

Suggested Hiring Summary

The ideal candidate should be a mid-level Python developer who can independently build and support AI-enabled backend services, rather than a pure data scientist or purely traditional backend engineer. The person should be comfortable working across Python development, GenAI/RAG workflows, GCP services, API integration, cloud deployment, and troubleshooting, while still being able to collaborate closely with senior engineers and architects on more complex solution design decisions.