Machine Learning Engineer

Position: ML Engineer – Machine Learning

Location: Manhattan, NYC – 3x a Week Onsite.

Duration: Full-time.

Role Overview

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.

Strong Python proficiency.

Experience with RAG and semantic search.

Hands-on OpenAI LLM integration experience.

Experience deploying systems in AWS (S3, Lambda, ECS/EKS, IAM).

Preferred Qualifications

Experience with Amazon Bedrock.

AWS data services: Glue, Athena, OpenSearch, and Aurora.

ETL/ELT pipeline development.

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.

Interview: 2-3 Rounds of Interview.