Senior Data Scientist Machine Learning Operations Gen AI - Remote

City/State
Virginia Beach, VA

Work Shift
First (Days)

Overview:

Sentara is hiring for a Senior Data Scientist!

This position is fully remote.

Overview

We are seeking a highly skilled and experienced Data Science ML Operations and Gen AI Engineer (or Senior) to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better healthcare.

The Senior Data Scientist will leverage data to improve healthcare outcomes and drive data-driven decision-making. Leveraging expertise in statistical analysis and machine learning, this role will collaborate with cross-functional teams to solve complex healthcare challenges and enhance patient care. This role will directly contribute to advancing medical research, optimizing healthcare processes, and delivering innovative solutions in the healthcare industry.

As a Senior ML Engineer on our team, you will play a crucial role in identifying gaps in our existing ML platform and architecting and building solutions to address those gaps. You will also collaborate with the AI team's ML Scientists and our partner data engineering and software development teams to bring ML AND Gen AI models to production and maintain their health and integrity while in production.

Your expertise in machine learning and Gen AI, coupled with a strong background in software development, will be instrumental in driving the success of Sentara's AI/ML initiatives.

Qualifications:

• 5+ years building production software/ML systems, including 1+ years of experience with LLMs/GenAI.

• Proficient in Python and one major DL/LLM stack (e.g., PyTorch/Transformers); experience with Lang Chain/Llama Index, vector DBs, and cloud (AWS/Azure/GCP).

• Demonstrated delivery of RAG, prompt engineering, evaluation frameworks, and guardrails in production.

• Strength in APIs, distributed systems, and ML Ops (K8s, CI/CD, monitoring).

• Experience with EPIC health platform is highly preferred

• Experience with ML platforms and ML Ops:
Demonstrated experience in assessing and improving ML platforms, identifying gaps, and architecting solutions to address them. Strong familiarity with ML platform components such as data ingestion, preprocessing, feature stores, model training, deployment, and monitoring.

• Experience with SQL and big data platforms such as Postgres, Redshift and Snowflake

• Experience with Agile/Scrum methodology and best practices

Preferred:

• Previous work experience with Generative AI and ML Ops in healthcare EPIC environment

• Understanding of use and implementation of Vector Databases

• Kubernetes container orchestration experience

Responsibilities

• Responsible for design and development of production-grade Machine Learning ops and Gen AI solutions

• Lead hands-on delivery of scalable GenAI solutions from problem framing prototyping evaluation production monitoring.

• Build internal copilots/assistants (knowledge search, code/content generation) and client-facing products (conversational analytics, summarization, recommendations, workflow automation).

• Design RAG pipelines, embedding strategies, vector search, and model orchestration; evaluate fine-tuning vs. prompt engineering.

• Implement guardrails, safety filters, prompt/version management, latency/throughput optimizations, and cost controls.

• ML platform and ML Ops:
Identify areas that require improvements or additional functionalities and use your expertise in machine learning and software engineering to architect and develop solutions that fill gaps in our ML platform and development ecosystem. Analyze system performance, scalability, and reliability to pinpoint opportunities for enhancement. Develop tools and solutions that help the team build, deploy, and monitor AI/ML solutions efficiently.

• System scalability and reliability:
Optimize the scalability, performance, and reliability and AI Team solutions by implementing best practices and leveraging industry-standard technologies. Collaborate with infrastructure teams to ensure smooth integration and deployment of ML solutions. Design scalable and efficient systems that leverage the power of machine learning for enhanced performance and capabilities.

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