Machine Learning Engineer

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, 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.