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AI Engineer/Sr data scientist

Network Objects Inc.United States🇺🇸United StatesPosted Oct 18, 2026

Why This Role Stands Out

Shape the future of AI by designing and deploying cutting-edge multi-agent LLM solutions on Azure, utilizing your expertise in data pipelines and prompt engineering. This hybrid role offers a fantastic opportunity for ambitious AI Engineers and Data Scientists to drive innovation within a reputable tech company and make a significant impact. You'll thrive here if you enjoy tackling complex challenges and delivering robust, enterprise-class AI systems.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
15 hours ago
SnowflakeAzureLLMPostgreSQL

Job Description

Design, build, and maintain data pipelines and integrations leveraging PostgreSQL Server and Snowflake for downstream AI and analytics use cases.

Own the end-to-end LLM application lifecycle from prompt engineering and retrieval workflow design through evaluation, observability, security, and performance tuning.

Design and implement a production-grade, enterprise-class multi-agent framework that is reliable, scalable, and maintainable in live environments.

Architect and deploy multi-agent, enterprise-class AI/agentic solutions on the Azure platform, aligning with Azure-native services and best practices.

Collaborate with cross-functional stakeholders to translate business requirements into robust, enterprise-class, production-ready AI system designs.

Ensure multi-agent LLM systems meet performance, security, and observability standards before and after deployment.

Communicate technical decisions and trade-offs clearly to both technical and non-technical partners.

Roles & Responsibilities

Demonstrated, hands-on experience designing, building, and deploying a production-grade, enterprise-class multi-agent framework in real-world systems.

Strong, hands-on experience with PostgreSQL Server and Snowflake, including schema design, query optimization, and data integration.

Solid understanding of the LLM application lifecycle: prompt engineering, retrieval-augmented workflows, evaluation methodologies, observability tooling, security considerations, and performance optimization.

Required hands-on experience with the Azure platform for building and deploying multi-agent, enterprise-class AI/agentic solutions.

Strong grasp of multi-agent architecture patterns (orchestration, coordination, agent-to-agent communication) at enterprise scale.

Ability to take ambiguous requirements through to delivered, production-ready, enterprise-class outcomes with minimal hand-holding.

Strong communication skills, with the ability to explain complex multi-agent system behavior to varied audiences.

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