AI/ML Engineer ($50/hr)

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

We are seeking an AI/ML Engineer to design, build, and deploy production-grade machine learning, generative AI, and agentic AI solutions. This role will focus on LLM applications, RAG pipelines, document intelligence, and reliable AI systems while partnering with product, clinical, and operational stakeholders.

Key Responsibilities

• Design and deploy LLM-powered and agentic AI applications, including tool/function calling, multi-agent orchestration, and MCP integrations.
• Build and optimize RAG pipelines using embeddings, vector databases, retrieval, re‑ranking, and grounding techniques.
• Develop document intelligence solutions for extraction, classification, OCR, and structured data generation.
• Build evaluation, human‑in‑the‑loop, and feedback processes to improve AI accuracy and reliability.
• Implement observability for AI/ML systems, monitoring quality, latency, cost, token usage, and model performance.
• Develop production‑grade Python services and deploy them using Docker, CI/CD, Kubernetes, and cloud‑native technologies.
• Partner with business, clinical, and operational teams to translate complex problems into scalable AI solutions.
• Apply Responsible AI, governance, security, evaluation, and model risk management practices.

Required Qualifications

• 5+ years of experience building and deploying ML/AI systems, including recent hands‑on experience with LLMs or agentic AI.
• Strong foundation in machine learning, deep learning, NLP, and transformer architectures.
• Expert‑level Python and experience with PyTorch and/or TensorFlow.
• Hands‑on experience with LLM platforms such as Azure OpenAI, OpenAI, or Anthropic.
• Experience with RAG, vector databases, embeddings, AI agents, and frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong SQL and experience building data/ML pipelines using tools such as Airflow, Spark, Databricks, or Snowflake.
• Experience with Docker, Git/GitHub, CI/CD, and cloud platforms, preferably Azure.
• Knowledge of ML/LLM evaluation and observability tools such as Arize, Langfuse, or similar.
• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent experience.

Preferred

• Experience in healthcare, insurance, or another regulated/high‑stakes environment.
• Knowledge of Responsible AI, model governance, validation, and AI security.
• Experience with Kubernetes, Terraform, Kafka, or infrastructure as code.
• Experience working with clinical or operational subject matter experts and designing AI solutions that support expert review and explainability.