Job Title: Machine Learning Engineer
Location: Manhattan NY
Duration: / Term: Contract
Experience Desired: 12 Years
Job Description:
We are seeking a 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
- 12 years of overall IT development experience with a strong background in backend and distributed systems.
- 7 years of experience in Machine Learning Data Engineering or Applied AI engineering.
- Strong proficiency in Python with experience building modular production-grade services.
- Proven experience implementing Retrieval-Augmented Generation (RAG) and semantic search architectures.
- Hands-on experience integrating and operationalizing OpenAI LLM APIs in production environments.
- Solid experience deploying and managing systems within AWS environments including services such as S3 Lambda ECS/EKS and IAM.
- Experience building scalable secure and observable AI/ML systems in production.
Qualifications Desired
- Experience working with Amazon SageMaker and/or Amazon Bedrock for model development deployment or managed LLM services.
- Strong familiarity with AWS data services including AWS Glue Amazon Athena Amazon OpenSearch Service and Amazon Aurora.
- Hands-on experience designing and implementing ETL/ELT data pipelines in cloud environments.
- Experience building LLM orchestration pipelines including reasoning workflows tool usage and multi-step agent architectures.
- Knowledge of LLM benchmarking evaluation frameworks and performance optimization (latency cost quality metrics).
- Experience integrating enterprise systems using SnapLogic.
- Exposure to Craxel Black Forest Time-Series Database (or similar time-series platforms); willingness to learn/train if not previously experienced.
- Experience implementing Infrastructure as Code (IaC) using AWS CDK CloudFormation or Terraform.
Key Skills:
Machine Learning LLM AWS Amazon SageMaker / Amazon Bedrock RAG Python
Job Title: Machine Learning Engineer Location: Manhattan NY Duration: / Term: Contract Experience Desired: 12 Years Job Description: We are seeking a ML Engineer LLM Platforms & Assistants will design build and operate production-grade large language model (LLM) pipelines primarily within AWS-...
Job Title: Machine Learning Engineer
Location: Manhattan NY
Duration: / Term: Contract
Experience Desired: 12 Years
Job Description:
We are seeking a 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
- 12 years of overall IT development experience with a strong background in backend and distributed systems.
- 7 years of experience in Machine Learning Data Engineering or Applied AI engineering.
- Strong proficiency in Python with experience building modular production-grade services.
- Proven experience implementing Retrieval-Augmented Generation (RAG) and semantic search architectures.
- Hands-on experience integrating and operationalizing OpenAI LLM APIs in production environments.
- Solid experience deploying and managing systems within AWS environments including services such as S3 Lambda ECS/EKS and IAM.
- Experience building scalable secure and observable AI/ML systems in production.
Qualifications Desired
- Experience working with Amazon SageMaker and/or Amazon Bedrock for model development deployment or managed LLM services.
- Strong familiarity with AWS data services including AWS Glue Amazon Athena Amazon OpenSearch Service and Amazon Aurora.
- Hands-on experience designing and implementing ETL/ELT data pipelines in cloud environments.
- Experience building LLM orchestration pipelines including reasoning workflows tool usage and multi-step agent architectures.
- Knowledge of LLM benchmarking evaluation frameworks and performance optimization (latency cost quality metrics).
- Experience integrating enterprise systems using SnapLogic.
- Exposure to Craxel Black Forest Time-Series Database (or similar time-series platforms); willingness to learn/train if not previously experienced.
- Experience implementing Infrastructure as Code (IaC) using AWS CDK CloudFormation or Terraform.
Key Skills:
Machine Learning LLM AWS Amazon SageMaker / Amazon Bedrock RAG Python
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