Senior Data Scientist, Applied AI and Agentic Solutions Engineer
New
J
JobgetherApplied AI and Data Science
Remote work opportunity within the United States.Full-TimeSenior
SalaryBase salary range of $84,000–$141,750, with actual compensation determined by experience and other job-related factors.
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Job Details
- Experience
- At least 6 years of experience
- Required Skills
- PythonCloud ComputingMachine LearningData scienceSoftware EngineeringGenerative AI
Requirements
- Bachelor’s degree in a quantitative discipline or equivalent professional experience.
- Minimum of 6 years of experience in analytics, data science, ML, or AI solution development.
- Demonstrated success developing and deploying AI and generative AI solutions for enterprise needs.
- Experience managing the full solution lifecycle from requirements gathering through deployment and adoption.
- Strong proficiency in Python.
- Familiarity with modern application frameworks such as React, TypeScript, and Node.js.
- Hands-on experience with LLMs, vector embeddings, and semantic search.
- Practical experience designing and deploying RAG architectures.
- Understanding of AI output evaluation including accuracy, security, and scalability.
- Experience with AI-assisted development and modern engineering practices like CI/CD and testing.
- Knowledge of production operations including monitoring and observability.
- Understanding of AI governance, privacy, and responsible AI principles.
Responsibilities
- Model complex business problems using statistical, algorithmic, machine learning, and data mining techniques.
- Partner with business and product stakeholders to define experiments and design solutions that address strategic needs.
- Develop and deploy generative AI and agentic solutions supporting enterprise automation and decision-making.
- Build Retrieval-Augmented Generation (RAG) solutions including document ingestion, chunking, and retrieval strategies.
- Develop software and automated processes to cleanse, integrate, and analyze large datasets.
- Evaluate AI outputs for accuracy, reliability, and security while optimizing models based on real-world outcomes.
- Maintain production-ready solutions with a focus on scalability, monitoring, and observability.
- Communicate complex analytical findings and technical recommendations to diverse business audiences.
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