Design and implement scalable AI and LLM applications
including RAG (Retrieval-Augmented Generation), embeddings, and multi-agent architectures for healthcare data and knowledge systems.
Architect and optimize enterprise Search solutions
using OpenSearch
(or Elasticsearch), including: Designing and maintaining
Universal Search Documents (USDs)
with rich metadata, relevance signals, and domain-specific ranking. Implementing efficient
indexing pipelines
and query optimization strategies for structured and unstructured healthcare data. Tuning search relevance using clickstream and behavioral
signals
, boosting, and custom scoring. Building
semantic search
layers using vector embeddings and hybrid retrieval (BM25 + ANN). Collaborate with data, engineering, and product teams to
integrate Search and ML pipelines
seamlessly into production. Establish and maintain
search performance monitoring
, relevance evaluation, and A/B experimentation frameworks. Lead teams in developing
responsible, explainable, and secure AI solutions
that comply with healthcare data standards and regulations. Mentor engineers and scientists, fostering technical excellence and innovation.
Contribute to architecture reviews, patents, and thought leadership in AI and Search innovation.
Required Qualifications
Master s or PhD in Computer Science, Data Science, or related technical field.
8+ years of experience in ML or AI engineering, with at least
2 years of hands-on experience in OpenSearch (or Elasticsearch)
. Proven track record delivering
end-to-end AI or search-based applications
in production. Strong proficiency in
Python
, PyTorch/TensorFlow
, and cloud platforms ( OCI, AWS, or Azure
). Deep understanding of
LLMs
, RAG pipelines
, and embedding-based search
. Experience creating, maintaining, and optimizing
Universal Search Documents (USDs)
including schema design, signals integration, and ranking models. Strong command of
query DSL
, aggregation frameworks, and performance optimization
for OpenSearch clusters (e.g., sharding, caching, and scaling strategies).
Preferred Qualifications
Experience integrating
LLMs with OpenSearch
for semantic and generative retrieval. Familiarity with
vector databases
, hybrid retrieval systems
, or cross-modal search
. Experience with
data pipeline orchestration
(Airflow, Prefect, or similar). Experience leading cross-functional AI/ML programs or search relevance initiatives.
Knowledge of healthcare data systems, interoperability, and compliance frameworks (e.g., HIPAA, FHIR).
Contributions to open-source search or ML communities, or published research in AI/ML.
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