Toptal: AI Engineer — AWS Bedrock AgentCore & Production Agentic Systems

Toptal: Full-Stack Engineer (Python + Temporal) | Remote | LATAM & Europe

July 27, 2026

CLEAResult: Marketing Automation Analyst

July 27, 2026

Published by Coupea on July 27, 2026

About the Role

We’re looking for engineers to help build and productionize AI systems on AWS Bedrock AgentCore — real conversational AI, RAG pipelines, and agent architectures that go well beyond proof-of-concept and serve live traffic and real users. 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 on Bedrock’s agentic stack. If you’ve built and shipped on AgentCore specifically — not just Bedrock in general — and want your work to run in production rather than sit in a notebook, this is built for that.

What You’ll Do

Design, build, and deploy conversational AI systems, chatbots, and AI agents using AWS Bedrock AgentCore
Architect and ship production-grade RAG (Retrieval-Augmented Generation) systems — not prototypes, but systems serving live traffic
Build and deploy LLM applications primarily on AWS Bedrock and AgentCore, with Azure OpenAI or equivalent platforms as secondary context
Develop and orchestrate agent architectures within AgentCore, using frameworks such as LangChain, LangGraph, or LlamaIndex where applicable
Build and maintain MCP (Model Context Protocol) server integrations to extend AgentCore agent capabilities
Design and build the production service layer around AgentCore (Lambda, API Gateway, IAM, DynamoDB, OpenSearch, or equivalents)
Establish CI/CD pipelines and manage development, beta, and production environments for AgentCore-based services
Implement observability for AgentCore agents: 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 Bedrock, AgentCore, and agentic AI systems, and bring relevant advances into the project

What You Bring

Proven, hands-on experience building and deploying AI agents on AWS Bedrock AgentCore in a production environment — not personal projects or tutorials
Direct experience with AWS Bedrock’s agentic tooling (AgentCore, Bedrock Agents, or equivalent Bedrock-native orchestration)
Strong Python skills for AI application development and/or service integration
Working experience with AWS cloud environments; Azure experience is a plus but not the primary requirement
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
Experience with Azure OpenAI or other non-AWS LLM platforms
Comfort working in an AI-assisted development environment using AI build and review tools