Machine Learning Engineer

New York Technology Partners; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

New York Technology Partners is seeking an ML Engineer to design, build, and operate production-grade LLM pipelines primarily within AWS. You will integrate OpenAI models into modular Python services, implement RAG and semantic search, and deploy scalable, observable AI assistants.

Ideal candidates have 4–7+ years in software or AI engineering, with hands-on AWS experience (S3, Lambda, ECS/EKS) and familiarity with LangChain. Knowledge of Bedrock or similar platforms is a plus.

Qualifications

• 4–7+ years in software, ML, or applied AI engineering.
• Experience with RAG and semantic search.
• Experience deploying systems in AWS (S3, Lambda, ECS/EKS, IAM).

Responsibilities

• Design and maintain LLM integrations using OpenAI APIs within AWS environments.
• Build Python-based LLM services deployed on AWS compute platforms (ECS, EKS, Lambda, or EC2).
• Implement RAG workflows and semantic search using AWS data and storage services.
• Develop LangChain or agentic workflows supporting reasoning and tool use.
• Integrate LLM pipelines with ETL/ELT workflows and enterprise data systems.

Skills

Python

LLM integration

RAG

AWS deployment

LangChain

Education

Bachelor's degree in CS/Engineering/IS

Tools

AWS (ECS/EKS/Lambda)

Job description

The ML Engineer – LLM Platforms & Assistants will design, build, and operate production-grade large language model (LLM) pipelines primarily within AWS-based environments. This role focuses on integrating OpenAI models into modular Python services, implementing Retrieval-Augmented Generation (RAG) and semantic search, and deploying scalable, secure, and observable AI assistants.

Key Responsibilities

• Design and maintain LLM integrations using OpenAI APIs within AWS environments.
• Build Python-based LLM services deployed on AWS compute platforms (ECS, EKS, Lambda, or EC2).
• Implement RAG workflows and semantic search using AWS data and storage services.
• Develop LangChain or agentic workflows supporting reasoning and tool use.
• Integrate LLM pipelines with ETL/ELT workflows and enterprise data systems.
• Deploy and integrate MCP servers and emerging orchestration tools.
• Apply AWS security best practices using IAM, KMS, and Secrets Manager.
• Implement monitoring and observability using CloudWatch and related tools.
• Migrate custom GPT solutions into production-grade AWS-hosted assistants.

Ideal Candidate Profile

• 4–7+ years in software, ML, or applied AI engineering.
• Experience with RAG and semantic search.
• Experience deploying systems in AWS (S3, Lambda, ECS/EKS, IAM).

Preferred Qualifications

• Experience with Amazon Bedrock.
• LLM orchestration and reasoning workflows.
• Benchmarking and LLM evaluation.
• Infrastructure as Code (CDK, CloudFormation, Terraform).

Education:

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent experience.