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 Staff 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
- Architect and evolve the AI platform infrastructure to support high-scale ML development, agentic frameworks, and self-serve pipelines.
- Drive the design and development of multi-agent systems for healthcare, including an extensible catalog of AI agents.
- Deliver high-availability, low-latency, production-ready AI applications.
- Lead the development and deployment of ML/DL models including transformers, GANs, and generative frameworks.
- Oversee scalable data pipelines and infrastructure needed for robust AI productization.
- Translate experimental results into actionable insights that inform product and business decisions.
- Guide strategic alignment of AI initiatives with long-term product and business goals.
- Mentor AI engineers and data scientists across teams, and lead code/design reviews.
- Work closely with leadership, product, and customers to shape AI strategies and solve high-impact problems.
What You Need
- masters or PhD in Computer Science, Engineering, or a related field.
- 9+ years in software development, with 6+ years of experience in AI/ML systems.
- At least 1+ year hands-on experience building generative AI pipelines, agents, and RAG systems.
Deep experience with:
- LLMs and prompt engineering
- Frameworks such as LangChain, CrewAI, Autogen (one or more)
- Vector databases like FAISS, ChromaDB
- Embedding models and Retrieval-Augmented Generation
- Proven track record of deploying AI/ML systems in production at scale.
- Strong understanding of classical ML techniques (Random Forests, SVMs, Boosting, etc).
- Deep proficiency in Python and enterprise application development.
- Experience with at least one cloud ML platform (Azure ML, Databricks, SageMaker).
- Familiarity with cloud infrastructure (AWS, Azure, GCP), distributed systems (Spark, Kubernetes), and MLOps best practices.
Preferred Skills
- Leadership in designing and scaling AI systems in production.
- Deep learning frameworks like PyTorch or TensorFlow.
- Strong foundation in applied mathematics and statistics.
- Experience integrating AI models as platform components
