Job Description

At Oracle Health, we are redefining healthcare intelligence through cutting-edge AI and Search technologies. As a

Principal Machine Learning Engineer

, you will be responsible for designing and operationalizing scalable ML and LLM-powered systems that enhance clinical insights, streamline operations, and power intelligent search experiences across our products.


You will serve as a

technical leader

, mentoring engineers and data scientists, defining solution architectures, and ensuring end-to-end reliability and performance in ML and Search pipelines.

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.