Senior Agentic AI Engineer

CodeRoad

. Design, develop, and scale autonomous agentic workflows and multi-agent systems using LangGraph, CrewAI, or Agent Development Kit (ADK) .

Posted 11/1/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying LLM-powered applications on AWS, utilizing advanced Python and integrating AI agents with enterprise systems. Proven ability to mentor teams and lead technical initiatives while ensuring security and performance in agentic workflows.

Highest-signal resume keywords

Advanced PythonAWS and Amazon BedrockLangGraph / CrewAIVector Databases (Pinecone or Weaviate)MCP Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Application EngineeringAgentic AI SystemsRAG ArchitectureEvaluation Frameworks for LLM GroundednessSecurity Measures Implementation

Soft Skills

Technical LeadershipMentoring DevelopersArticulating Complex Architecture

Tools & Technologies

LangSmithLangtraceAgentOpsN8n

Industry Keywords

Cloud DeploymentMicroservicesTask-Success MetricsHallucination ChecksPrompt-Injection Protection

Tech Stack

Tools & technologies

AWSCloudMicroservicesPython

About the role

Key responsibilities & impact

Design, develop, and scale autonomous agentic workflows and multi-agent systems using LangGraph, CrewAI, or Agent Development Kit (ADK)
Own cloud deployment and security architecture of LLM-powered applications on AWS using Amazon Bedrock and scalable microservices
Connect AI agents to enterprise APIs, data sources, and deterministic tools via Model Context Protocol (MCP) and workflow platforms such as n8n
Build high-performance RAG pipelines and vector database integrations using Pinecone or Weaviate for contextual retrieval and reranking
Implement end-to-end evaluation, guardrails, and security measures, including task-success metrics, hallucination checks, and prompt-injection protection
Lead system observability using LangSmith, Langtrace, or AgentOps
Mentor engineering PODs in Python and AI development best practices
Transform business requirements into scalable, secure, and reliable agentic solutions on AWS
Guide nearshore development teams through the production AI deployment lifecycle

Requirements

What you’ll need

5+ years in professional software engineering
2–3+ years dedicated to LLM application engineering and agentic AI systems
Advanced Python
AWS and Amazon Bedrock
LangGraph / CrewAI
Vector databases including Pinecone or Weaviate
RAG architecture
MCP integration
Hands-on experience with LangSmith, Langtrace, or AgentOps
Experience with evaluation frameworks for LLM groundedness and accuracy
Strong ownership mindset and technical leadership
Ability to mentor developers and articulate complex architecture to non-technical stakeholders
Advanced English (written and spoken) is mandatory

Benefits

Comp & perks

100% remote: Work from anywhere in Latin America
Observe local national holidays
Generous PTO policies for rest and personal time
Financial support for healthcare coverage
Competitive USD compensation pegged to the US Dollar
Continuous career evolution within high-impact global projects