Identity AI / ML Engineer

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 For applications and inquiries, contact: hirings@openkyber.com