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Prompt Engineering Specialist
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Job Description
- Req#: 26-01025
Prompt Engineering Specialist
Connecticut- Hybrid
Mandatory areas - ML Engineer - Prompt engineer
Must Have skills:
• Prompt engineering
• Snowflake cortex
• Healthcare domain knowledge
• Llms
• Gpt
• Prompt design
• Chain-of-thought
Prompt Engineering Specialist - Prompt Engineering Specialist to support the Medicare STARS data science team in designing, optimizing, and managing AI prompts for analytics and agentic workflows on the Snowflake platform. Key Responsibilities Design and optimize prompts for LLM-based workflows within Snowflake's agentic DTI platform Collaborate with data science and business teams to translate Medicare STARS use cases into effective AI prompts Build and test prompt pipelines for proactive member insights, intervention flagging, and score tracking Iterate on prompt performance to improve accuracy and reliability of AI-generated outputs Document prompt libraries and best practices for reuse across the team Required Skills Hands-on experience with LLMs (GPT, Claude, or similar) Strong understanding of prompt design patterns and chain-of-thought techniques Familiarity with Snowflake Cortex or similar AI/ML platforms Healthcare domain knowledge (Medicare STARS a plus) Python or SQL for data interaction
Under guidance from Senior ML Engineers, design and develop ML models that provides accurate results with controls to solve the business problem identified, using state of art techniques.
1. Work with cross functional teams - business, technology, and product teams to understand the product vision and build ML solutions that provides brings value to the product
2. Executes relevant data wrangling activities related to the problem to create viable dataset
3. Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem
4. Fine tune the baseline model for optimum performance
5. Test Models internally per acceptance criteria from the business
6. Identify areas and techniques to optimize the model based on test results
7. Document relevant artefacts for communicating with the business
8. Work with data scientists to deploy the models.
9. Work with product teams in planning and execution of new product releases.
10. Set OKRs and success steps for self/ team and provide feedback of goals for team membersAbout the company
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