AI Engineer

Title - AI Engineer

Location:    
NY/NJ – Office based
Dallas – Office based

Introduction:

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.

Responsibilities:

• Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution following MCP protocol.
• 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.
• Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies.
• Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation.
• Build a RAG pipeline: Curate domain-knowledge, establish feedback loops, and maintain knowledge freshness.
• Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures.

Requirements:

Essential Skills:

1. 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
2. 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
3. Practical experience with Large Language Models (LLMs) and API integration.
4. Understanding of different LLMs, both commercial and open source, and their capabilities.
5. Solid grasp of applied statistics, core ML concepts, algorithms, and data structures.
6. Strong analytical problem-solving, ownership, and urgency.
7. Preferred: Proficiency building and operating on cloud infrastructure (AWS), including containerized services, serverless, data services, orchestration, model serving, and infra-as-code.

Rejection List (Disqualifiers):

• Missing 5+ years of software development experience.
• Missing strong hands-on experience with Python-based application development.
• Missing 3+ years building and operating production ML systems.
• Missing practical LLM application development experience.
• Missing experience building RAG pipelines and agentic AI solutions.
• Missing understanding of commercial and open-source LLM ecosystems.
• Missing AI safety, governance, or production reliability experience.
• Unable to design, deploy, and support production-grade AI systems.