Senior Software Engineer & AI Prompt Engineer (GenAI Full Stack)

About Pacific Health Group

At Pacific Health Group, we are at the forefront of revolutionizing healthcare. You will play a vital role in this mission. We are dedicated to improving health outcomes by addressing social determinants of health and coordinating comprehensive community-based services, particularly through our programs. If you are passionate about making a difference and thrive in a dynamic, mission-driven environment, we invite you to join our team.

Overview

The Senior Software Engineer & AI Prompt Engineer is responsible for the end-to-end design, development, and optimization of AI-powered software systems across Pacific Health Group. This role combines full-stack engineering with advanced prompt engineering to build scalable, production-grade Generative AI solutions, including LLM integrations, Retrieval-Augmented Generation (RAG) pipelines, and agentic AI systems.

This position owns the full lifecycle of AI applications—from architecture and prompt design to deployment, monitoring, and continuous optimization—ensuring performance, reliability, and measurable operational impact. The role requires both hands-on technical execution and leadership in establishing AI engineering standards, best practices, and system scalability.

Core Areas of Responsibility

1. AI & Prompt Engineering

• Design, develop, and optimize prompts, chains, and workflows for LLM-based applications. 
• Build and manage RAG pipelines, including ingestion, preprocessing, chunking, embeddings, indexing, retrieval, and evaluation. 
• Architect and implement agentic AI systems, including multi-step reasoning, tool usage, orchestration, and multi-agent patterns. 
• Establish prompt evaluation frameworks, benchmarking processes, and continuous improvement loops for quality, latency, and cost. 
• Apply fine-tuning and domain adaptation techniques where necessary. 
• Ensure responsible AI practices, including governance, safety, and reliability in production environments. 

2. Software Engineering & Full Stack Development

• Design and build scalable backend systems using Python (FastAPI or similar) and/or Java-based architectures. 
• Develop modern front-end applications using React, Angular, or Vue frameworks. 
• Build and maintain APIs, microservices, and integrations across internal and external systems. 
• Ensure system performance, scalability, observability, and reliability across the stack. 
• Contribute to architectural decisions and long-term technical roadmap development. 

3. Data & AI Infrastructure

• Design and manage data pipelines (ETL) to support AI workflows and model performance. 
• Implement and maintain vector databases and embedding-based retrieval systems. 
• Deploy, monitor, and optimize AI models in production environments. 
• Optimize inference performance, scalability, and cost efficiency. 
• Leverage cloud platforms (AWS, GCP, Azure) and AI services such as Vertex AI. 

4. Architecture & Technical Leadership

• Translate business requirements into scalable architectures, technical specifications, and delivery plans. 
• Lead development efforts, including system design, code reviews, and engineering best practices. 
• Mentor team members and promote a culture of engineering excellence and AI adoption. 
• Collaborate cross-functionally with product, operations, and leadership teams to deliver impactful AI solutions. 
• Evaluate emerging AI tools, frameworks, and technologies for practical implementation. 

5. Quality, Testing & Deployment

• Develop and implement testing strategies across application and AI layers, including unit, integration, regression, and performance testing. 
• Maintain CI/CD pipelines and ensure production readiness of all systems. 
• Monitor system performance, manage incidents, and drive continuous optimization. 
• Ensure high standards of reliability, scalability, and consistency across releases. 

6. Compliance, Data Security & Governance (PHI, HIPAA, and AI Safety)

• Ensure all AI systems, data pipelines, and integrations comply with HIPAA and organizational data security standards. 
• Implement safeguards for handling Protected Health Information (PHI), including encryption, access control, and secure transmission. 
• Establish audit trails, monitoring systems, and governance frameworks for AI usage and data handling. 
• Conduct risk assessments related to AI systems, data exposure, and model behavior. 
• Collaborate with compliance and security teams to enforce best practices in data protection and AI governance. 

Authority & Accountability

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