Senior Data Scientist Machine Learning Operations Gen AI - Remote

City/State

Work Shift

Overview:

This position is fully remote.

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 LangChain/LlamaIndex, 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.

· Data processing and workflow pipelines: Streamline data ingestion, preprocessing, feature engineering, and model training workflows to improve efficiency and reduce latency. Work with data engineering and data platform teams to design and implement robust data pipelines that support the AI team’s needs.

· Model deployment and monitoring: Evaluate and optimize model prototypes for real-world performance. Work with infrastructure and development teams to integrate ML models into production systems. Work closely with partner teams to communicate and understand technical requirements and challenges.

· As part of Sentara’s Data Science team you will be responsible for implementation and operationalization of AI/ML models. You will work with other machine learning engineers, data scientists, software engineers and platform engineers to ensure success of the AI/ML implementations. Specific responsibilities will include:

· Apply software engineering rigor and best practices to machine learning, including AI/MLOPs, CI/CD, automation, etc.

· Take offline models data scientists build and turn them into a real machine learning production system.

• Bachelor's Degree (Required)

• No specific certification or licensure requirements

• Required to have 5+ years of experience as a Data Scientist with a strong focus on Azure and Microsoft Data Science, AI, and machine learning toolsets.
• Required to have strong problem-solving skills and the ability to tackle complex healthcare challenges using data-driven approaches. Can help the Data Science infrastructure building up, working with ML Ops team for model implementation, mentoring and developing junior staff.
• Required to have strong proficiency in data analysis, data manipulation, and data visualization using Python.
• Required to have familiarity with healthcare-related datasets, medical terminologies, and electronic health records (EHR) data.
• Required to have knowledge of statistical techniques, hypothesis testing, and experimental design for healthcare research.
• Required to have strong machine learning expertise: Proficient in machine learning algorithms, statistical modeling, and data analysis. Hands-on experience with standard ML frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, XGBoost, TensorFlow, or Keras).
• Required to have solid understanding of data engineering principles, data structures, and algorithms. Proficient in Python and/or other programming languages commonly used in ML development.
• Required to have experience in technologies, frameworks and architecture like Java or Python, Angular, React, JSON, Application Servers, CI/CD is preferred.
• Required to have experience with one or more AI automations platforms like Kubeflow pipeline, MLFlow, Azure Pipeline, AWS Sage Maker Pipeline, Airflow, Jenkins, Spark, Hadoop, Kafka, Jira and GIT.

We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for full-time employment is: $91,416.00 - $152,380.80.  Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities.

Keywords: #Indeed, Talroo-IT, #LI-DS1, #Talroo - Healthplan, #Talroo

Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves.

In support of our mission “to improve health every day,” this is a tobacco-free environment.

For positions that are available as remote work, Sentara Health employs associates in the following states:

Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.