athenahealth - Senior Technical Staff Engineer - Generative AI

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.

- Bachelors 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.