Asks for

The ad says“Hands-on expertise with agentic orchestration tools including LangGraph, CrewAI, Agent Development Kit, and N8N.”
The ad says“Experience with AI observability tools like LangSmith, AgentOps, or Langtrace.”
The ad says“Strong foundational knowledge of cloud environments (AWS, Azure, or GCP) and MLOps tools (MLflow, Kubeflow).”
The ad says“Strong foundational knowledge of cloud environments (AWS, Azure, or GCP) and MLOps tools (MLflow, Kubeflow).”
The ad says“Hands-on expertise with agentic orchestration tools including LangGraph, CrewAI, Agent Development Kit, and N8N.”
The ad says“Strong foundational knowledge of cloud environments (AWS, Azure, or GCP) and MLOps tools (MLflow, Kubeflow).”
The ad says“Strong foundational knowledge of cloud environments (AWS, Azure, or GCP) and MLOps tools (MLflow, Kubeflow).”
The ad says“Hands-on expertise with agentic orchestration tools including LangGraph, CrewAI, Agent Development Kit, and N8N.”
The ad says“Experience with AI observability tools like LangSmith, AgentOps, or Langtrace.”
The ad says“Experience with AI observability tools like LangSmith, AgentOps, or Langtrace.”
The ad says“Strong foundational knowledge of cloud environments (AWS, Azure, or GCP) and MLOps tools (MLflow, Kubeflow).”
The ad says“Deep proficiency in Python and core AI/ML concepts (reinforcement learning, multi-agent systems, RAG, tool-calling frameworks).”
The ad says“Hands-on expertise with agentic orchestration tools including LangGraph, CrewAI, Agent Development Kit, and N8N.”
The ad says“Proven track record with vector databases such as Pinecone, Weaviate, or similar vector indices.”
The ad says“Deep proficiency in Python and core AI/ML concepts (reinforcement learning, multi-agent systems, RAG, tool-calling frameworks).”
The ad says“Deep proficiency in Python and core AI/ML concepts (reinforcement learning, multi-agent systems, RAG, tool-calling frameworks).”
The ad says“Deep proficiency in Python and core AI/ML concepts (reinforcement learning, multi-agent systems, RAG, tool-calling frameworks).”
The ad says“Deep proficiency in Python and core AI/ML concepts (reinforcement learning, multi-agent systems, RAG, tool-calling frameworks).”
The ad says“Proven track record with vector databases such as Pinecone, Weaviate, or similar vector indices.”

About CodeRoad

CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape.

About the Role

As a Senior Agentic AI Engineer, you will anchor the design, orchestration, and deployment of next-generation autonomous AI systems. You will serve as the technical backbone of our AI practice, taking full ownership of end-to-end agentic workflows—from integrating Multi-Cloud Platforms (MCP) and configuring vector datastores to engineering resilient multi-agent orchestration frameworks. Beyond writing production-grade code, you will drive architectural standards, ensure rigorous observability, and mentor cross-functional teams to push the boundaries of modern AI engineering.

This role is critical to scaling our high-impact AI capabilities, enabling clients to transition from basic automation to fully autonomous, context-aware decision engines. By embedding ethical AI frameworks, robust monitoring pipelines, and intuitive user interfaces, you will ensure our agentic solutions remain performant, transparent, and aligned with client business objectives.

Key Responsibilities

Lead the architectural design and technical execution of scalable, secure, and resilient agentic AI systems while mentoring junior engineering talent.
Build advanced multi-agent workflows and autonomous decision engines using frameworks such as LangGraph, CrewAI, Agent Development Kit, and N8N.
Engineer and maintain high-throughput data pipelines powered by vector databases (Pinecone, Weaviate) to enable precise high-dimensional retrieval and RAG systems.
Optimize production models by establishing real-time observability and evaluation pipelines using Langtrace, AgentOps, and LangSmith to mitigate latency, drift, and bias.
Develop intuitive front-end interfaces using Streamlit and Chainlit, leveraging React knowledge to enhance user experience and interaction with autonomous agents.
Embed ethical AI principles into the development lifecycle, incorporating robust guardrails for explainability, transparency, and regulatory compliance.

Requirements

5+ years of overall software development experience, with at least 2–3 years dedicated to developing production AI/ML systems and agentic architectures.
Deep proficiency in Python and core AI/ML concepts (reinforcement learning, multi-agent systems, RAG, tool-calling frameworks).
Hands-on expertise with agentic orchestration tools including LangGraph, CrewAI, Agent Development Kit, and N8N.
Proven track record with vector databases such as Pinecone, Weaviate, or similar vector indices.
Experience with AI observability tools like LangSmith, AgentOps, or Langtrace.
Strong foundational knowledge of cloud environments (AWS, Azure, or GCP) and MLOps tools (MLflow, Kubeflow).
Strong soft skills: Ownership mindset, cross-functional collaboration, strategic problem-solving, and structured technical leadership.
Advanced English (written and spoken) with the ability to communicate complex AI concepts to non-technical stakeholders clearly.

What You’ll Love

100% Remote work environment across Latin America.
Holidays Off according to your local calendar.
Paid Time Off (PTO).
Health Insurance Assistance Program.
Competitive Compensation in USD.
Growth Opportunities in a fast-scaling nearshore software powerhouse.