The technology that once promised to simplify patient care has brought more issues than anyone ever anticipated. At Innovaccer, we defeat this beast by making full use of all the data Healthcare has worked so hard to collect, and replacing long-standing problems with ideal solutions.
Data is our bread and butter for innovation. We are looking for a Senior AI Engineer who understands healthcare data and can leverage the data to build algorithms to personalize treatments based on the clinical and behavioral history of patients. We are looking for a superstar who will define and build the next generation of predictive analytics tools in healthcare.
A Day in the Life
- Design and build scalable AI platform architecture to support ML development, agentic frameworks, and robust self-serve AI pipelines.
- Develop agentic frameworks and a catalog of AI agents tailored to healthcare use cases.
- Design and deploy high-performance, low-latency AI applications.
- Build and optimize ML/DL models, including generative models like Transformers and GANs.
- Construct and manage data ingestion and transformation pipelines for scalable AI solutions.
- Conduct experiments, and statistical analysis, and derive insights to guide development.
- Collaborate with data scientists, engineers, product managers, and business stakeholders to translate AI innovations into real-world applications.
- Partner with business leaders and clients to understand pain points and co-create scalable AI-driven solutions.
What You Need
- Master s in Computer Science, Engineering, or a related field.
- 5+ years of software engineering experience with strong API development skills.
- 3+ years of experience in data science and at least 1+ year in building generative AI pipelines, agents, and RAG systems.
- Strong Python programming skills with enterprise application development and optimization.
- Experience with:
- LLMs, prompt engineering, and fine-tuning SLMs.
- Frameworks like LangChain, CrewAI, or Autogen (at least one is a must).
- Vector databases (e.g., FAISS, ChromaDB).
- Embedding models and Retrieval-Augmented Generation (RAG) design.
- Familiarity with at least one ML platform (Databricks, Azure ML, SageMaker).
