AI Engineer

Role Name/ Team:

PRX- AI Engineer

NY/NJ Office based Dallas
- Office based Any other US location
- Remote

Any visa try to avoid H1bs
-If its really good candidate we can try

In this role you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose reason and take actions in production environments to improve productivity and address issues related to production support.

• Build agentic AI systems:
  Design and implement tool-calling agents that combine retrieval structured reasoning and secure action execution (function calling change orchestration policy enforcement) following MCP protocol. Engineer robust guardrails for safety compliance and least-privilege access.
• Productionize LLMs:
  Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines prompt synthesis response validation and self-correction loops tailored to production operations.
• Integrate with runtime ecosystems:
  Connect agents to observability incident management and deployment systems to enable automated diagnostics runbook execution remediation and post-incident summarization with full traceability.
• Collaborate directly with users:
  Partner with production engineers and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability risk reduction and cost; and deliver auditable business-aligned outcomes.
• Safety reliability and governance:
  Build validator models adversarial prompts and policy checks into the stack; enforce deterministic fallbacks circuit breakers and rollback strategies; instrument continuous evaluations for usefulness correctness and risk.
• Scale and performance:
  Optimize cost and latency via prompt engineering context management caching model routing and distillation; leverage batching streaming and parallel tool-calls to meet stringent SLOs under real-world load.
• Build a RAG pipeline:
  Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
• Raise the bar:
  Drive design reviews experiment rigor and high-quality engineering practices; mentor peers on agent architectures evaluation methodologies and safe deployment patterns

understand what skills experience and qualities you are looking for.

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