Posted 1w ago

Associate AI Engineer

@ Greater New York Mutual Insurance
Edison, New Jersey, United States
$69k-$124k/yrHybridFull Time
Responsibilities:Design, Develop, Deploy
Requirements Summary:Bachelor’s degree in Computer Science or related field; 1–3 years AI/ML, data engineering, or software engineering; hands-on with LLMs/GenAI; cloud experience on Azure; API development; CI/CD; strong Python; Azure OpenAI and related Azure services experience.
Technical Tools Mentioned:Python, Azure OpenAI, Azure Functions, Logic Apps, AKS, CI/CD, Git, LLMs, RAG, Vector databases, APIs, Automation services
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Job Description

Remote position with the ability to travel to our NJ and NY locations up to 25% of the time.

 

Position Summary: The position is responsible for designing, building, deploying, and maintaining enterprise-grade Agentic AI solutions under the direction of an Sr. AI Engineer. This role focuses on implementing autonomous and semi-autonomous AI agents using large language models (LLMs), orchestration frameworks, and Azure-native AI services.

The position supports intelligent automation initiatives across Insurance business domains by translating product requirements into scalable, secure, and observable AI systems.

 

Essential Duties and Responsibilities:

  • Design, develop, and deploy Agentic AI systems based on product requirements defined by the Business SMEs.
  • Implement agent workflows including:
    • Planning, execution, validation, and retry logic
    • Tool and API invocation
    • Context and memory management
  • Build and maintain LLM-powered pipelines, including RAG-based architectures.
  • Integrate AI agents with enterprise systems using APIs, event-driven workflows, and automation services.
  • Develop and manage data ingestion and enrichment pipelines for structured and unstructured data.
  • Monitor AI system performance, accuracy, latency, and cost; implement optimizations and improvements.
  • Implement guardrails, safety mechanisms, and human-in-the-loop controls.
  • Support production deployment using Azure-native services and CI/CD pipelines.
  • Document system architecture, workflows, prompts, and operational runbooks.
  • Collaborate with product managers, architects, and data teams to iterate on AI solutions.
  • Participate in special projects and perform additional duties as required.