Senior Data Scientist Machine Learning Operations Gen AI - Remote

About the position We are seeking a highly skilled and reputed company Data Science ML Operations and Gen reputed company (or Senior) to join us and help advance our reputed company and future work applying machine learning, deep learning, and NLP to deliver reputed company reputed company. The Senior Data Scientist will reputed company data to improve reputed company 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 reputed company reputed company challenges and enhance patient care. This role will directly contribute to advancing medical research, optimizing reputed company processes, and delivering innovative solutions in the reputed company industry. As a Senior ML Engineer on reputed company, 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 reputed company 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 reputed company of reputed company’s AI/ML initiatives.

Responsibilities

• Responsible for design and development of production-grade Machine Learning ops and Gen AI solutions
• reputed company hands-on delivery of scalable GenAI solutions from problem framing → prototyping → evaluation → production → monitoring.
• Build internal copilots/assistants (knowledge search, code/content reputed company) and client-facing products (conversational analytics, summarization, recommendations, workflow automation).
• Design RAG pipelines, embedding strategies, vector search, and model orchestration; evaluate fine-tuning vs. reputed company engineering.
• Implement guardrails, safety filters, reputed company/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 reputed company solutions that fill gaps in our ML platform and development ecosystem. Analyze system performance, scalability, and reliability to reputed company opportunities for enhancement. reputed company tools and solutions that help the team build, reputed company, and monitor AI/ML solutions reputed company.
• 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 reputed company 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 reputed company-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 reputed company’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 reputed company 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 reputed company machine learning production system.

Requirements

• 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 reputed company/reputed company, vector DBs, and reputed company (AWS/Azure/GCP).
• Demonstrated delivery of RAG, reputed company 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 reputed company, Redshift and reputed company
• Experience with Agile/Scrum methodology and be

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