ML Engineer – LLM Platforms & Assistants

Location: NYC ( HYBRID )
Employment Type: C2C / Full-TIme
Experience: 4-7+ years

Role Overview

The ML Engineer will design, build, and operate production-grade large language model (LLM) pipelines, primarily in AWS-based environments. The 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 that support 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.

Required Skills

• 4-7+ years of experience in software, ML, or applied AI engineering.
• Strong Python proficiency.
• Hands-on experience integrating OpenAI LLMs.
• Experience with RAG and semantic search.
• Experience deploying systems on AWS (S3, Lambda, ECS/EKS, IAM).

Preferred Skills

• Amazon Bedrock.
• AWS data services: Glue, Athena, OpenSearch, Aurora.
• ETL/ELT pipeline development.
• LLM orchestration and reasoning workflows (LangChain, agentic frameworks).
• Benchmarking and LLM evaluation.
• Infrastructure as Code (CDK, CloudFormation, Terraform).