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Senior Gen AI/ML Engineer

Posted 10 days agoViewed

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💎 Seniority level: Senior, 5+ years

📍 Location: United States

💸 Salary: 140000.0 - 165000.0 USD per year

🔍 Industry: Insurance

🏢 Company: Integrated Specialty Coverages, LLC

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: AWSDockerPythonSQLArtificial IntelligenceData AnalysisGitMachine LearningMLFlowPyTorchAPI testingTensorflowCommunication SkillsProblem SolvingData visualization

Requirements:
  • 5+ years of relevant model development and deployment experience, preferably within the insurance industry
  • Proven experience in prompt engineering, LLM fine-tuning, and LLM evaluation
  • Experience working with RAG architecture, including embedding models, retrieval mechanisms, and integration with LLMs
  • Proven experience building and deploying machine learning models
  • Experience building & deploying models with AWS tools (Bedrock, Sagemaker, Comprehend, S3, etc.) is preferred
  • Proficiency in writing production-quality, scalable code using Python; experience with scikit-learn, PyTorch, TensorFlow, huggingface, Keras, etc.
  • Strong working knowledge of MLFlow, Docker, Git, and SQL
  • Experience in creating and integrating APIs for model deployment
Responsibilities:
  • Utilize expertise in generative AI technologies to develop innovative solutions for underwriting, risk assessment, claims processing, and customer engagement.
  • Think big about the arc of development of generative AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems
  • Work closely with engineering and devops teams to ensure seamless integration, deployment, and scalability of machine learning models within the existing infrastructure.
  • Keep abreast of the latest developments in AI and machine learning, particularly in generative models, to inform strategy and implementation.
  • Build, deploy, maintain, and optimize machine learning models
  • Adhere to best practices for model validation, testing, and monitoring to ensure performance and reliability
  • Provide guidance and mentorship to junior data scientists, fostering a culture of learning and professional growth within the data science team.
  • Effectively communicate complex analytical findings to both technical and non-technical audiences through reports, presentations, and visualizations.
  • Proactively identify areas for improvement in existing models and processes, advocating for and implementing enhancements.
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