Agentic AI Engineer

Fractal Analytics•New York, NY

1d•$120,000 - $140,000•Remote

About The Position

We are seeking a highly skilled Agentic AI Engineer to design and build intelligent automation solutions for large-scale enrollment and benefits administration systems. The platform currently processes over 15 million enrollment-related transactions, with approximately 80% of scenarios handled through deterministic rule-based automation and the remaining 20% requiring manual intervention. The objective of this role is to leverage Agentic AI, LLMs, and machine learning techniques to automate complex exception handling, accelerate rule creation, reduce testing effort, and continuously evolve the decisioning framework as new scenarios emerge in production. The ideal candidate will have strong expertise across AI/ML engineering, full-stack development, MLOps, workflow orchestration, and rule-engine modernization.

Requirements

Strong experience in Machine Learning, Generative AI, and Agentic AI systems.
Experience building AI agents using: LangGraph, LangChain, OpenAI / Anthropic / Azure OpenAI ecosystems
Knowledge of probabilistic reasoning and AI-driven decision systems.
Expert-level Python development skills.
Experience building production-grade applications and APIs.
Strong software engineering practices including testing, CI/CD, and code quality.
Experience with business rule engines and decision management platforms.
Understanding of DMN (Decision Model and Notation) and XML-based rule representations.
Experience with model deployment, monitoring, and lifecycle management.
Knowledge of containerization and cloud-native architectures.
Familiarity with ML observability and evaluation frameworks.
Strong analytical and problem-solving skills.
Experience working with large-scale transactional systems and operational data.

Nice To Haves

Experience in healthcare, insurance, benefits administration, or enrollment platforms.
Experience processing high-volume transactional workloads (10M+ transactions).
Knowledge of decision intelligence and business process automation.
Experience implementing AI-assisted software testing frameworks.
Familiarity with hybrid deterministic + LLM-based decision systems.
Experience with PyDMN or similar decision automation frameworks.

Responsibilities

Design and develop Agentic AI solutions to automate complex enrollment change scenarios currently requiring manual intervention.
Build AI-powered agents capable of analyzing exceptions, recommending actions, and creating new rule definitions from emerging production scenarios.
Develop scalable orchestration frameworks using LangGraph, LangChain, and modern AI agent architectures.
Work on next-generation rule-based systems that combine deterministic business rules with probabilistic AI reasoning.
Convert and operationalize business rules represented in DMN/XML formats using tools such as PyDMN.
Enable continuous learning mechanisms where new production cases can be analyzed and translated into new business rules.
Develop AI-assisted test case generation frameworks for highly complex rule environments containing hundreds of business scenarios and edge cases.
Identify unseen exception paths and generate test scenarios that may not have been anticipated by SMEs.
Reduce testing cycles and accelerate deployment through intelligent validation and simulation.
Build models and decision-support systems for scenario classification, exception handling, and rule recommendation.
Evaluate the stability, reliability, and performance of LLM-driven workflows.
Design feedback loops for continuous improvement of AI-generated recommendations.
Implement scalable MLOps pipelines for model deployment, monitoring, and governance.
Build observability and evaluation frameworks for AI agents and rule engines.
Ensure reliability, scalability, and compliance in production environments processing millions of transactions.