9 Athenahealth Technology Jobs

Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.

Responsibilities may include, but are not limited to:

50% [Primary Function] Technical Execution

• Contribute to accurate, unambiguous technical design specifications, including GenAI system integration and AI-enhanced workflows.
• Deliver customer value in the form of high-quality AI-powered software components and services, ensuring adherence to security, performance, longevity, and AI-driven automation best practices.
• Estimate the size of development tasks in story points, considering LLM inference latency and AI API rate limits.
• Understand and follow coding conventions, architectures, and best practices for GenAI-powered applications, LLM prompt engineering, and RAG (Retrieval-Augmented Generation) models.
• Write, debug, and deploy code to production, ensuring timely fixes for GenAI-based APIs, embeddings, and AI-driven microservices.
• Integrate and optimize OpenAI, Azure OpenAI, Hugging Face, LangChain, and LlamaIndex into enterprise applications.
• Leverage vector databases (Pinecone, FAISS, ChromaDB) for similarity search and AI retrieval pipelines.
• Adhere to Definition of Done (DOD) as part of the sprint, including:
  • Unit tests, functional testing
  • LLM performance benchmarking (BLEU, ROUGE, cosine similarity)
  • AI model validation API response optimization (temperature, top-k, max tokens)
  • Code reviews, bug fixes, documentation
  • Adherence to AI governance responsible AI practices

30% Contributions to the Team

• Learn domain-specific AI applications, including GenAI capabilities in automation, search, and AI-assisted decision-making.
• Take ownership of AI-enhanced product features, ensuring continuous model improvement and fine-tuning strategies.
• Contribute to agile ceremonies with a focus on AI-driven solutions and optimizations.
• Volunteer for GenAI-focused backlog items, such as:
  • RAG model refinement
  • Prompt engineering for better accuracy
  • LLM evaluation and response optimization
• Participate in scrum meetings (daily stand-ups, sprint planning, readouts, retrospectives) with a focus on AI model iteration and feature scaling.
• Drive self-organization in AI workflows, ensuring GenAI is used effectively across teams.

10% Cross functional Coordination and Communication

• Work collaboratively across Technology, Product, AI/ML, and DevOps teams to align AI-driven enhancements with business goals.
• Build strong relationships with AI engineers, data scientists, and cloud architects to optimize LLM-based applications.
• Ensure AI compliance with security, ethical AI policies, and privacy standards (HIPAA, GDPR, SOC2, AI governance best practices).

10% Mentorship of Others

• Train and mentor developers on GenAI integration, AI API usage, embeddings, and vector search optimizations.
• Guide the team on LLM prompt engineering, RAG model improvements, and API latency optimization.
• Encourage adoption of AI-enhanced developer workflows (e.g., Copilot, AI-assisted code generation, AI-powered testing).

Education, Experience, Skills Required:

• 5-10 years of experience in an engineering role, with exposure to AI/ML concepts.
• Experience in an Agile environment preferred.
• Bachelor s Degree or equivalent in Computer Science, Engineering, or related field.
• Strong software engineering experience, including AI model integration and GenAI API workflows.
• Knowledge of modern programming language: Python (preferred for AI applications)
• Familiarity with Unix/Linux, Big Data, SQL, NoSQL, and AI data pipelines.
• Experience with AI frameworks and APIs such as OpenAI GPT, Hugging Face Transformers, LangChain, LlamaIndex.
• Exposure to retrieval-augmented generation (RAG), embeddings, and AI search optimization techniques.
• Understanding of vector databases (FAISS, Pinecone, ChromaDB) and similarity search models.
• Proficiency in cloud-based AI deployments (AWS or Azure OpenAI).
• Strong grasp of GenAI model evaluation techniques (BLEU, ROUGE, BERT Score, cosine similarity metrics).

Behaviors Abilities Required:

• Ability to design and implement AI-powered solutions that improve software functionality.
• Problem-solving mindset to debug and optimize AI-generated responses.
• Ability to collaborate across AI, DevOps, and software engineering teams for seamless AI model integration.
• Experience in AI-driven feature development, from prompt engineering to embedding optimization.
• Ability to assess AI-generated content for bias, accuracy, and compliance.
• Strong analytical skills to measure AI model performance and recommend improvements.
• Curiosity and eagerness to explore new AI models, tools, and best practices for scalable GenAI deployment.