AI/Platform Engineer — AWS Bedrock Agentcore

Job Overview

toptal.

com/ About the Role We're looking for engineers to help build and productionize AI systems that go well beyond proof-of-concept - real conversational AI, RAG pipelines, and agent architectures running on AWS Bedrock and/or Azure OpenAI, serving live traffic and real users.

Job Description

Whether your strength is on the AI application side (agents, RAG, orchestration) or the platform side (deployment, observability, security), this role sits at the center of turning working demos into production-grade, reliable systems.

If you like being close to the frontier of agentic AI and want your work to ship rather than sit in a notebook, this is built for that.

To apply:

Key Responsibilities

• .toptal.com/ About the Role We're looking for engineers to help build and productionize AI systems that go well beyond proof-of-concept - real conversational AI, RAG pipelines, and agent architectures running on AWS Bedrock and/or Azure OpenAI, serving live traffic and real users.
• What You'll Do Design, build, and deploy conversational AI systems, chatbots, and AI agents powered by large language models Architect and ship production-grade RAG (Retrieval-Augmented Generation) systems - not prototypes, but systems serving live traffic Build and deploy LLM applications on AWS Bedrock, Azure OpenAI, AgentCore, or equivalent platforms Develop and orchestrate agent architectures using frameworks such as LangChain, LangGraph, or LlamaIndex Build and maintain MCP (Model Context Protocol) server integrations to extend agent capabilities Design and build the production service layer (Lambda, API Gateway, IAM, DynamoDB, OpenSearch, or equivalents) Establish CI/CD pipelines and manage development, beta, and production environments Implement observability: tracing, dashboards, per-turn cost and latency metrics, error rates, and audit trails Implement key security controls - data-leakage protection, session isolation, auth/authz boundaries, secure prompt/response storage Write clean, maintainable, production-quality Python across the AI application and platform stack Monitor, evaluate, and iterate on agent, RAG, and platform performance in production Stay current with fast-moving developments in LLMs, agentic systems, and cloud AI platforms, and bring relevant advances into the project What You Bring Proven experience building conversational AI systems, AI agents, or AI platform infrastructure in production - not personal projects or tutorials Hands-on experience with LLM application platforms: AWS Bedrock, Azure OpenAI, AgentCore, or similar Strong Python skills for AI application development and/or service integration Working experience with AWS and/or Azure cloud environments Experience with at least one of: agent orchestration frameworks (LangChain, LangGraph, LlamaIndex), RAG system design, or AWS production infrastructure (Lambda, API Gateway, IAM, DynamoDB, OpenSearch) Experience with observability and monitoring for AI or distributed systems Strong understanding of security, data handling, and production-readiness tradeoffs Comfortable working in a fast-moving, evolving technical environment with pragmatic engineering judgment Nice to Have Experience with MCP servers Experience with infrastructure-as-code (CDK, CloudFormation) and CI/CD pipeline design Experience with distributed data tools such as Apache Spark, PySpark, or AWS EMR Experience with Amazon SageMaker or similar ML platforms Experience with OpenSearch vector search administration for RAG workloads Experience building data pipelines for AI evaluation and KPI extraction Comfort working in an AI-assisted development environment using AI build and review tools RATE: $30-$50/hr.

Required Skills and Qualifications

• What You'll Do Design, build, and deploy conversational AI systems, chatbots, and AI agents powered by large language models Architect and ship production-grade RAG (Retrieval-Augmented Generation) systems - not prototypes, but systems serving live traffic Build and deploy LLM applications on AWS Bedrock, Azure OpenAI, AgentCore, or equivalent platforms Develop and orchestrate agent architectures using frameworks such as LangChain, LangGraph, or LlamaIndex Build and maintain MCP (Model Context Protocol) server integrations to extend agent capabilities Design and build the production service layer (Lambda, API Gateway, IAM, DynamoDB, OpenSearch, or equivalents) Establish CI/CD pipelines and manage development, beta, and production environments Implement observability: tracing, dashboards, per-turn cost and latency metrics, error rates, and audit trails Implement key security controls - data-leakage protection, session isolation, auth/authz boundaries, secure prompt/response storage Write clean, maintainable, production-quality Python across the AI application and platform stack Monitor, evaluate, and iterate on agent, RAG, and platform performance in production Stay current with fast-moving developments in LLMs, agentic systems, and cloud AI platforms, and bring relevant advances into the project What You Bring Proven experience building conversational AI systems, AI agents, or AI platform infrastructure in production - not personal projects or tutorials Hands-on experience with LLM application platforms: AWS Bedrock, Azure OpenAI, AgentCore, or similar Strong Python skills for AI application development and/or service integration Working experience with AWS and/or Azure cloud environments Experience with at least one of: agent orchestration frameworks (LangChain, LangGraph, LlamaIndex), RAG system design, or AWS production infrastructure (Lambda, API Gateway, IAM, DynamoDB, OpenSearch) Experience with observability and monitoring for AI or distributed systems Strong understanding of security, data handling, and production-readiness tradeoffs Comfortable working in a fast-moving, evolving technical environment with pragmatic engineering judgment Nice to Have Experience with MCP servers Experience with infrastructure-as-code (CDK, CloudFormation) and CI/CD pipeline design Experience with distributed data tools such as Apache Spark, PySpark, or AWS EMR Experience with Amazon SageMaker or similar ML platforms Experience with OpenSearch vector search administration for RAG workloads Experience building data pipelines for AI evaluation and KPI extraction Comfort working in an AI-assisted development environment using AI build and review tools RATE: $30-$50/hr.

Work Location and Schedule

This role is listed as Remote USA with location information shown as Remote USA. The employment type is Full Time.

About the Company

Toptal is the organization connected with this listing. USA Jobs Today displays this opportunity for job discovery only, so applicants should verify company details, application instructions, and eligibility on the official employer website.