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# SR GEN AI Engineer

- **Company:** Propertyvalue Quantum Technologies Llc
- **Location:** Minneapolis, MN, United States
- **Experience:** Expert
- **Salary:** $180,960.0
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Information Systems, Data Infrastructure, Software Debugging, Python (Programming Language), Search Technologies, Data Streaming, Feature Store, Chatbots, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Generative AI, Agentic-AI, Data Lakes, Kubernetes, Information Technology, Build Tools, Machine Learning Operations, Prompt Frameworks, Invoking Functions, Automation Anywhere, Databricks, Microservices
- **Published:** October 14, 2026
- **Apply:** https://www.dice.com/job-detail/734452de-9464-434b-a38b-c20411c8c24b

## About the Role

* Hands-on experience with LLMs and RAG architectures
 * Experience with vector databases and semantic search
 * Practical understanding of prompt engineering and evaluation

2) Agentic AI Engineers (Autonomous AI Systems), * Hands-on experience building agentic or multi-step AI workflows (not just prompting)

 * Experience with orchestration frameworks and tool calling
 * Understanding of AI safety, guardrails, and reliability patterns
 * Ability to operationalize AI agents at enterprise scale, * Strong hands-on background in GenAI and enterprise architecture
 * Deep understanding of LLM, RAG, Agentic AI, and deployment patterns
 * Experience coaching teams through AI adoption and transformation
 * Ability to translate business goals into technical solutions, 1. Experience in designing, building, and maintaining the data infrastructure that powers machine learning systems - pipelines, feature stores, data lakes, and real-time streaming architectures that feed model training and inference at scale.
 2. Led enterprise-wide architectural governance and helped create standards/frameworks driving efficiency.
 3. Strong experience in Databricks, Delta Lake, Apache Spark, Python, and Apache Airflow.

Top 3 Preferred Skills

 1. Any related certification
 2. Worked in the life and annuities industry

Education: At least a bachelor's degree (or equivalent experience) in Computer Science, Software/Electronics Engineering, Information Systems, or a closely related field is required for the project

## Description

Design, build, and deploy production-grade Generative AI solutions using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and enterprise integrations., * Build end-to-end GenAI applications using LLMs (text, documents, QA, summarization, extraction)

 * Implement RAG pipelines, data ingestion, chunking, embeddings, vector search, and grounded responses
 * Develop APIs and microservices to integrate GenAI solutions with enterprise systems
 * Implement structured outputs, function calling, and prompt frameworks
 * Support evaluation, testing, monitoring, and continuous improvement of GenAI solutions
 * Collaborate closely with the GenAI Architect and Agentic AI Engineers, Build Agentic AI systems that go beyond chatbots - autonomous, goal-driven AI agents that can reason, plan, call tools, and execute multi-step workflows., * Design and implement agentic AI workflows (planning, execution, observation, iteration)
 * Build tool-using AI agents integrated with APIs, data sources, and enterprise systems
 * Implement multi-agent or orchestrated agent architectures where required
 * Combine Agentic AI with RAG for grounded, enterprise-safe responses
 * Implement guardrails, safety checks, and evaluation mechanisms
 * Debug, optimize, and operationalize agentic workflows in production, Act as the technical anchor and AI coach for the program - defining the GenAI architecture, standards, and guardrails, while guiding developers and ensuring long-term scalability., * Define the overall GenAI reference architecture (LLM, RAG, Agentic AI, data, security)
 * Establish standards for prompt design, RAG patterns, evaluation, testing, and deployment
 * Guide and mentor GenAI and Agentic AI engineers on design and implementation choices
 * Drive architecture and design reviews; ensure reusability across regions and teams
 * Define governance, guardrails, and responsible AI practices
 * Act as the bridge between business stakeholders and technical teams