Senior Agentic AI Engineer
New
C
CodeRoadAI engineering
Source API remote eligibility restrictions: Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bermuda, Bolivia, Bonaire, Sint Eustatius and Saba, Brazil, Canada, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Greenland, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Pierre and Miquelon, Saint Vincent and the Grenadines, Sint Maarten (Dutch part), Suriname, Trinidad and Tobago, Turks and Caicos Islands, United States, United States Minor Outlying Islands, Uruguay, Venezuela, Virgin Islands, British, Virgin Islands, U.S.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English (written and spoken) is mandatory.
- Experience
- 5+ years in professional software engineering, with 2–3+ years dedicated to LLM application engineering and agentic AI systems.
- Required Skills
- AWSPython
Requirements
- Have 5+ years of professional software engineering experience.
- Have 2–3+ years dedicated to LLM application engineering and agentic AI systems.
- Have advanced Python skills.
- Have experience with AWS and Amazon Bedrock.
- Have experience with LangGraph or CrewAI and agentic AI systems.
- Have experience with vector databases such as Pinecone or Weaviate, RAG architecture, and MCP integration.
- Have hands-on experience with LangSmith, Langtrace, or AgentOps and LLM evaluation frameworks for groundedness and accuracy.
- Demonstrate an ownership mindset, technical leadership, and the ability to mentor developers and explain complex architecture to non-technical stakeholders.
- Have advanced written and spoken English.
Responsibilities
- Design, develop, and scale autonomous agentic workflows and multi-agent systems using LangGraph, CrewAI, or ADK.
- Own cloud deployment and security architecture for LLM-powered applications on AWS using Amazon Bedrock and scalable microservices.
- Connect AI agents to enterprise APIs, data sources, and deterministic tools using MCP and workflow platforms such as n8n.
- Build RAG pipelines and vector database integrations with 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.
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