Ranger Technical Resources
AI/ML Tech Lead #2585
Position Summary
Our partner, one of the U.S.’s most respected healthcare systems, is rethinking how practical machine learning and AI blend within a development-driven environment. As the lead technical contact, you ll help teams apply both AI tooling and ML models to build smarter applications and workflows. You ll provide guidance on data usage, model integration, LLM adoption, and intelligent automation across multiple development groups. The systems you support are data -heavy and influence real operational and clinical decisions. You ll prototype, experiment, and introduce practical solutions that improve productivity and insight. This is a hands‑on role with broad visibility and real user impact, ideal for someone who enjoys working at the intersection of AI, machine learning, and engineering.
Experience and Education
BS in Computer Science, Data Science, Information Technology, or related field
Background in Software Development, AI engineering, or machine learning within complex product environments
Experience supporting multiple engineering teams or working within large-scale software organizations
Hands‑on work with both AI tooling (LLMs, copilots) and traditional ML development practices
Exposure to data-rich systems that support operational or analytical decision‑making
Familiarity with cloud‑native development environments
Skills and Strengths
Python
Machine Learning
LLMs integration
AI Tooling
JavaScript
React / Next.js
Node.js / NestJS
Data Analysis & Modeling Concepts
Model Evaluation
Algorithmic Thinking
APIs
SQL
Cloud Architecture
Testing Automation
System Design
Version Control
CI/CD Pipeline
Primary Job Responsibilities
Guide teams on using AI tooling and ML practices to accelerate development, testing, and research
Integrate AI‑driven and ML‑driven features into existing and new applications
Build proofs‑of‑concept showcasing how AI/ML can improve productivity, insight, and decision‑making
Collaborate with product, data, and engineering leads to shape long‑term AI/ML strategy and roadmaps
Translate complex AI and ML concepts into practical engineering guidance and workflows
Ensure AI models, ML pipelines, and automated workflows remain reliable, safe, and scalable
Contribute to core software and application development when needed
Review architecture and provide direction on AI/ML‑enabled patterns and best practices
Drive technical decision‑making around model usage, integration, deployment, and performance
Promote responsible and ethical approaches to AI and ML adoption within engineering teams
Support the rollout and evaluation of LLM platforms like Claude and other emerging AI tools
#J-18808-Ljbffr
Experience and Education
BS in Computer Science, Data Science, Information Technology, or related field
Background in Software Development, AI engineering, or machine learning within complex product environments
Experience supporting multiple engineering teams or working within large-scale software organizations
Hands‑on work with both AI tooling (LLMs, copilots) and traditional ML development practices
Exposure to data-rich systems that support operational or analytical decision‑making
Familiarity with cloud‑native development environments
Skills and Strengths
Python
Machine Learning
LLMs integration
AI Tooling
JavaScript
React / Next.js
Node.js / NestJS
Data Analysis & Modeling Concepts
Model Evaluation
Algorithmic Thinking
APIs
SQL
Cloud Architecture
Testing Automation
System Design
Version Control
CI/CD Pipeline
Primary Job Responsibilities
Guide teams on using AI tooling and ML practices to accelerate development, testing, and research
Integrate AI‑driven and ML‑driven features into existing and new applications
Build proofs‑of‑concept showcasing how AI/ML can improve productivity, insight, and decision‑making
Collaborate with product, data, and engineering leads to shape long‑term AI/ML strategy and roadmaps
Translate complex AI and ML concepts into practical engineering guidance and workflows
Ensure AI models, ML pipelines, and automated workflows remain reliable, safe, and scalable
Contribute to core software and application development when needed
Review architecture and provide direction on AI/ML‑enabled patterns and best practices
Drive technical decision‑making around model usage, integration, deployment, and performance
Promote responsible and ethical approaches to AI and ML adoption within engineering teams
Support the rollout and evaluation of LLM platforms like Claude and other emerging AI tools
#J-18808-Ljbffr