ML Platform Engineer

We are seeking a ML Engineer LLM Platforms & Assistants who 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., * 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.

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., * 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