AI/ML Engineer

Role Overview:

We are seeking a highly skilled AI/ML Engineer to lead the development, scaling, and productionization of our advanced AI Agent ecosystem. In this role, you will be responsible for orchestrating multi-agent LLM systems and developing machine learning models to analyze complex clinical data for our maternal-care platform.

You will directly oversee and mature three critical intelligent agents:

Agent 1

production billing reconciliation and payer eligibility),

Agent 2

navigation automation), and

Agent 3

clinical pattern recognition executing against an 11K\+ escalation corpus).

Key Responsibilities:

• Agent Orchestration:

Design, build, and optimize multi-agent workflows using TypeScript/Node.js to call enterprise LLM APIs.

• ML Pattern Recognition:

Develop and train specialized Python-based machine learning pipelines to ingest and detect anomalies, trends, and risk indicators within an 11K\+ clinical escalation corpus.

• Agent Lifecycle Management:

Maintain and iteratively improve

Agent 1

(cross-reconciliation across PCM/BHI/RPM/CCM), advance

Agent 2

through its deployment phases, and mature

Agent 3

from build to production readiness.

• Data Pipeline Integration:

Work closely with the data engineering team to process structured and unstructured data via

Snowflake (Snowpark / Python APIs)

and ensure data compliance with HIPAA standards for handling Protected Health Information (PHI).

• System Performance \& Evaluation:

Establish strict evaluation frameworks (evals) for LLM outputs to guarantee clinical safety, accuracy, and mitigation of hallucinations in triage recommendation queues.

Requirements

Required Technical Skills \& Qualifications:

• Languages:

Advanced proficiency in

Python

(for ML data science workloads) and

TypeScript / Node.js

(for backend orchestration and API integration).

• AI/LLM Frameworks:

Strong experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, or LangGraph) and commercial/open-source LLM APIs.

• Machine Learning NLP:

Deep understanding of Natural Language Processing (NLP), text embedding generation, vector databases, and pattern-recognition techniques applied to unstructured text datasets.

• Data Stack:

Hands-on experience with

Snowflake

and

Snowpark

using Python APIs.

• Healthcare Domain (Highly Preferred):

Familiarity with US healthcare compliance, HIPAA data privacy requirements, and navigating clinical nomenclature.