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# Senior AI Engineer - Applied AI & Data Science

- **Company:** Benefitfocus, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $174,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Application Performance Management, ARM Architecture, Automation of Tests, Business Software, Business Systems, Cloud Computing, Databases, Continuous Integration, Information Retrieval, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Performance Tuning, Recommender Systems, Standard Sql, Search Technologies, Software Deployment, Software Engineering, SQL Databases, Unstructured Data, Enterprise Software Applications, Chatbots, Sql Optimization, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, IT Architecture, Vector Embeddings, Generative AI, Agentic-AI, Git, Build Management, Information Technology, Machine Learning Operations, Evaluation of Large Language Models, Artificial Intelligence Governance, Restful APIs
- **Published:** October 14, 2026
- **Apply:** https://www.careerjet.com/job/usdee9786a211fc191e81239a6bc1fd619/eaa

## About the Role

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, Statistics, or a related technical discipline, or equivalent work experience.
AI Engineering
· Proven experience building and deploying production AI or GenAI solutions.
· Strong understanding of LLMs and modern AI architectures.
· Hands-on experience with:
o Retrieval-Augmented Generation (RAG)
o Semantic Search
o Vector Embeddings
o AI Agents
o Prompt Engineering
o LLM Evaluation
o AI Observability & Monitoring
o AI Guardrails
Snowflake Expertise
Hands-on experience with:
· Snowflake Cortex AI
· Cortex Search
· Cortex Agents
· Snowpark Python
· Snowflake ML
· SQL Optimization & Performance Tuning
· Enterprise-scale Snowflake Development
Software Engineering
Strong experience with:
· Python
· SQL
· REST APIs
· Git
· CI/CD
· Automated Testing
· Cloud-Native Development
· Enterprise Software Engineering Best Practices
Data Exploration & Analytics
· Experience exploring large enterprise datasets to uncover business insights and opportunities.
· Strong understanding of structured and unstructured data.
· Experience building AI-ready data models and pipelines.
· Ability to assess data quality and AI suitability.
· Familiarity with machine learning concepts and evaluation methodologies.
Business & Communication
· Excellent analytical and problem-solving skills.
· Ability to translate ambiguous business problems into scalable AI solutions.
· Strong stakeholder management and executive communication skills.
· Ability to independently drive initiatives from ideation through production deployment.
Preferred Knowledge & Experience
Preferred qualifications include experience that helps the candidate accelerate impact in enterprise AI delivery, platform engineering, and business partnership.
· Design and deploy enterprise AI applications that solve real business problems.
· Build scalable RAG, semantic search, and agentic AI solutions.
· Develop reusable AI capabilities that accelerate future product development.
· Improve enterprise productivity through AI-powered automation and intelligent assistants.
· Help define AI architecture standards and engineering best practices.

## Description

We are seeking a highly motivated Senior AI Engineer to design, build, and deploy enterprise-scale AI applications on the Snowflake AI Data Cloud. This role combines AI engineering, enterprise data exploration, software engineering, and applied analytics to transform business problems into production-ready AI solutions.
This opportunity is appropriate for candidates who can work in a remote or hybrid environment, depending on business needs, team alignment, and applicable location requirements. The ideal candidate is passionate about building intelligent systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and modern AI engineering frameworks.
The Contributions You'll Make
Enterprise AI Application Development
· Design, develop, and deploy production-grade AI applications using Snowflake Cortex AI, Cortex Search, Cortex Agents, Snowpark Python, and Snowflake ML.
· Build enterprise copilots, conversational AI assistants, intelligent search applications, recommendation systems, and AI-powered business workflows.
· Develop reusable AI components, frameworks, and services.
· Take AI solutions from concept through production deployment and continuous improvement.
AI Discovery & Data Exploration
· Explore structured and unstructured enterprise datasets to identify AI opportunities.
· Analyze data quality, relationships, business context, and knowledge gaps.
· Assess enterprise data readiness for RAG, semantic search, vector retrieval, and agentic AI.
· Design metadata, embeddings, and knowledge structures that improve AI performance.
· Translate ambiguous business challenges into scalable AI solutions.
Retrieval-Augmented Generation & Knowledge Systems
· Design and implement enterprise RAG architectures.
· Build semantic search and vector retrieval pipelines.
· Develop enterprise knowledge retrieval systems.
· Optimize grounding, relevance, retrieval quality, and response accuracy.
· Improve AI systems through prompt engineering, retrieval tuning, and evaluation.
AI Engineering & Agentic AI
· Design and build AI agents that reason across enterprise knowledge and business systems.
· Develop multi-step workflows and agent orchestration pipelines.
· Integrate LLMs with enterprise APIs, databases, and business applications.
· Implement human-in-the-loop workflows and approval mechanisms.
· Build secure, governed, and scalable AI services.
Platform & Software Engineering
· Develop scalable applications using Python, SQL, Snowpark, and modern software engineering practices.
· Build REST APIs and enterprise integrations.
· Implement CI/CD pipelines and automated testing.
· Optimize application performance, scalability, reliability, and cost.
AI Evaluation & Responsible AI
· Develop evaluation frameworks for prompts, retrieval, and model performance.
· Monitor AI quality through quantitative and qualitative evaluation techniques.
· Implement governance, explainability, guardrails, and responsible AI practices.
· Improve AI applications using production metrics and user feedback.
Business Partnership & Innovation
· Partner directly with business and technology stakeholders to identify high-value AI opportunities.
· Translate business challenges into scalable AI architectures.
· Prototype innovative AI solutions and validate business value.
· Present AI concepts and recommendations to technical and executive audiences.
· Help establish enterprise AI architecture standards, engineering guidelines, and best practices.
Minimum Knowledge & Experience