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# Sr AI Engineer

- **Company:** Coterie Insurance
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $115,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Code Review, Continuous Integration, Python (Programming Language), Machine Learning, Natural Language Processing, Open Source Technology, Cloud Services, Standard Sql, Software Engineering, Systems Architecture, Software Organization, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, Fastapi, Pytest, AI Platforms, Kubernetes, Information Technology, Data Analytics, Software Version Control, Docker, Databricks
- **Published:** May 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3b8d8eb077b81cae

## About the Role

Do you have experience in Version control?, * Advanced degree or equivalent professional experience in Artificial Intelligence, Computer Science, Data Science, Economics, Engineering, Machine Learning, Mathematics, Operations Research, Statistics or a related quantitative field

 * 4+ years of professional experience deploying AI/ML-based applications to production
 * Intellectual curiosity
 * Technical proficiency with large language models, prompt engineering, embeddings, vector databases, retrieval-augmented generation or agentic workflows
 * Technical proficiency in Python including extensive experience with Pytest, Pydantic and FastAPI
 * Technical proficiency with tools/technologies such as AWS/Azure/GCP, Claude, Databricks, Docker, Gemini, Kubernetes, LangChain, LangGraph, Lucid, OpenAI and SQL
 * Technical proficiency with production-grade development practices including documentation, testing, version control, code review, CI/CD (GitActions) and observability
 * Experience designing and deploying systems that account for data privacy, security, access controls and responsible AI considerations
 * Experience combining ML and LLM approaches in a single system
 * Familiarity with acquiring data from the web at scale including scraping, proxy management and working with public APIs, aggregators and open-source datasets
 * A bias toward practical, measurable outcomes and the ability to iterate quickly while maintaining high engineering standards
 * A clear point of view on the trade-offs between Anthropic, Google and OpenAI models across different task types
 * Experience building/leveraging reusable AI services or shared tooling that increases operational efficiencies

## Description

The Senior AI Engineer will work within the Research & Development (R&D) subdomain of Coterie's broader Data and Analytics (DnA) department to design, build, test and deploy intelligent systems that enhance business operations, customer and product capabilities. This role combines applied machine learning, software engineering and responsible AI practices to deliver reliable, scalable and secure AI-enabled solutions in production environments., * Partner with DnA teammates, software engineers and business stakeholders to deliver high-impact, AI-enabled solutions

 * Create AI capabilities into internal tools, customer-facing products and operational workflows using APIs, cloud services and modern software development practices
 * Implement secure and compliant AI systems that align with company policies, regulatory expectations and responsible AI principles
 * Architect/design, build and productionalize AI and machine learning solutions including generative AI, natural language processing, predictive modeling and automation workflows
 * Develop modularized/templatized code structures; new requests are config changes not rewrites
 * Help mentor early career data scientists and AI engineers
 * Evaluate multiple LLM providers (Anthropic, Google, OpenAI) and determine the best fit for each use case based on quality, latency, cost and reliability
 * Establish monitoring and feedback loops for continuous improvement
 * Document technical decisions, system architectures and model assumptions
 * Stay current on emerging AI tools, frameworks, model capabilities and industry best practices; recommend and implement pragmatic adoption strategies


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