The company is looking to solve the problem of creating a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all by leveraging AI and GenAI technologies to enhance software components, services, and workflows.
Requirements
- 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).
- Knowledge of modern programming language: Python (preferred for AI applications)
- Familiarity with Unix/Linux, Big Data, SQL, NoSQL, and AI data pipelines.
Responsibilities
- 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.
Other
- 5-10 years of experience in an engineering role, with exposure to AI/ML concepts.
- Experience in an Agile environment preferred.
- 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.
