AI/ML Engineer — LLMs, RAG Pipelines & Production Systems

New York Technology Partners; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

New York Technology Partners seeks an AI/ML Engineer to design, build, and deploy production-grade ML, generative AI, and agentic AI solutions focused on LLM applications, RAG pipelines, document intelligence, and reliable AI systems.

You will partner with product, clinical, and operational stakeholders to translate complex problems into scalable AI solutions while applying Responsible AI, governance, and model risk practices.

Qualifications

• 5+ years building and deploying ML/AI systems with hands-on LLM experience.
• Strong ML, DL, NLP, and transformer foundations.
• Proficient Python with PyTorch or TensorFlow.
• Hands-on with Azure OpenAI, OpenAI, or Anthropic platforms.
• Experience with LLM agents, RAG, vector databases, and embeddings.

Responsibilities

• Design and deploy LLM-powered and agentic AI applications.
• Build and optimize RAG pipelines and document intelligence solutions.
• Develop production-grade Python services and deploy with Docker, Kubernetes, and CI/CD.
• Implement observability for AI/ML systems and monitor performance.
• Collaborate with clinical, operational, and product teams on scalable AI solutions.
• Apply Responsible AI, governance, and model risk practices.

Skills

LLM development

Python

PyTorch

TensorFlow

LangChain (or LangGraph)

RAG pipelines

Docker

Kubernetes

CI/CD

Cloud platforms (Azure)

Observability tools

Education

Bachelor's degree in CS/DS/ML or related

Master's degree in CS/DS/ML or related

Tools

Docker

Kubernetes

Airflow

Spark

Databricks

Snowflake

GitHub

Job description

New York Technology Partners seeks an AI/ML Engineer to design, build, and deploy production-grade ML, generative AI, and agentic AI solutions focused on LLM applications, RAG pipelines, document intelligence, and reliable AI systems.

You will partner with product, clinical, and operational stakeholders to translate complex problems into scalable AI solutions while applying Responsible AI, governance, and model risk practices.