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:
80% [Primary Function] Technical Execution
- Contribute to accurate, unambiguous technical design specifications with appropriate detail, including GenAI-based system integration.
- Deliver customer value in the form of high-quality software components and AI-powered services while ensuring adherence to security, performance, longevity, and AI model integration best practices.
- Estimate the size of development tasks in story points.
- Understand and follow coding conventions, AI model integration architectures, and best practices for working with LLMs (Large Language Models).
- Write, debug, and deploy code to production, ensuring timely fixes and optimizations for GenAI-powered applications.
- Adhere to Definition of Done (DOD) as part of the sprint, including unit tests, functional testing, code reviews, API performance validation, AI model evaluation, and prompt tuning.
- Implement and optimize GenAI-based workflows (e.g., OpenAI APIs, LangChain, vector databases for RAG, AI-driven automation).
10% Contributions to the Team
- Learn the domain knowledge for the assigned area, including how GenAI solutions can improve product offerings.
- Take ownership of GenAI-powered features and AI-driven automation post-release, ensuring continuous improvement.
- Participate in AI use case discussions, including LLM fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and AI-based automation.
- Contribute to agile ceremonies to improve team performance.
- Volunteer for work in the backlog, including GenAI model integration tasks, and commit to high-quality delivery.
- Participate in scrum meetings (daily stand-ups, sprint planning, readouts, and retrospectives).
- Help determine how GenAI is leveraged effectively in collaboration with cross-functional teams.
5% Cross functional Coordination and Communication
- Work collaboratively across the Technology, Product, and AI/ML teams to ensure alignment toward GenAI integration goals.
- Build strong relationships with AI engineers, data scientists, and cross-functional team members to leverage AI capabilities in products.
5% Mentorship of Others
- Share knowledge on GenAI best practices, AI integration strategies, and API-based AI services with team members.
- Participate in AI/ML discussions, helping others learn how to integrate and optimize GenAI models in software applications.
Education, Experience, & Skills Required:
- 2-4 years of experience in a software engineering role, with exposure to AI/ML concepts.
- Familiarity with working in an Agile environment preferred.
- Bachelor's Degree or equivalent in Computer Science, Engineering, or a related field.
- Software engineering experience, with exposure to AI model integration and generative AI frameworks.
- Knowledge of modern programming language such as: Python - preferred for AI applications
- Familiarity with Unix/Linux, Big Data, SQL, NoSQL, and modern data storage technologies.
- Experience with AI APIs and frameworks such as OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, and vector databases (FAISS, Pinecone, ChromaDB).
- Exposure to object-oriented programming, RESTful APIs, distributed computing, WebU, and modern JavaScript frameworks.
- Understanding of GenAI model deployment in cloud environments (AWS or Azure).
- Familiarity with AI model evaluation techniques, including embeddings, retrieval-augmented generation (RAG), and prompt engineering.
Behaviors & Abilities Required:
- Ability to learn and adapt in a fast-paced environment, particularly in AI and GenAI integration.
- Ability to write performant, scalable, and maintainable code for AI-powered applications.
- Critical thinking skills to assess AI-generated outputs and improve model reliability.
- Problem-solving mindset to identify alternative methods of solving software engineering and AI-related challenges.
- Proven track record of delivering AI-integrated features while following best practices in software development.
- Ability to collaborate on AI-driven solutions, working with cross-functional teams, data scientists, and software engineers.
- Curiosity and willingness to explore new AI models, tools, and frameworks for enhancing software applications with GenAI capabilities.
