Agentic AI Engineer
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What you'll do
- Design and deploy a hierarchical multi-agent workflow using LangGraph, successfully transitioning legacy single-prompt implementations into modular agentic systems.
- Build domain-specific sub-agents featuring dynamic, on-demand context loading to significantly optimize latency and token economics.
- Design and integrate scalable tool-calling capabilities and standardized connectors using the Model Context Protocol (MCP).
- Optimize LLM observability, reasoning traces, and cost-tracking systems by setting up and managing LangFuse.
- Lead the establishment of automated evaluation harnesses and regression testing suites using tools like PromptFlow or PromptFoo against benchmark datasets.
- Collaborate on implementing strict security guardrails and access controls following OWASP Top 10 standards for LLM applications.
What they're looking for
- 5+ years of professional software development experience, with a primary focus on Python.
- 2+ years of hands-on experience building production AI agents or complex LLM workflows using LangGraph or LangChain.
- Tech Stack: Strong practical experience with modern foundation models (Anthropic Claude, OpenAI, Google Gemini), open-weights models, and advanced prompt engineering techniques.
- Observability & Eval: Hands-on experience with LLM observability platforms (e.g., LangFuse) and automated evaluation frameworks.
- Infrastructure: Familiarity with containerized cloud environments including Azure and Docker.
- Ecosystem: Hands-on experience with the Microsoft 365 Agents SDK.
- Soft Skills: High ownership mindset, strong problem-solving initiative, and an empathetic, collaborative team approach.
- Language: Advanced English (written and spoken) is mandatory.
Nice to have
- Experience working with Retrieval-Augmented Generation (RAG) pipelines and vector database integrations (e.g., Pinecone, Weaviate, Qdrant).
- Familiarity with enterprise AI security frameworks, data privacy compliance, and prompt injection mitigation strategies.
- Exposure to serverless architectures and microservices deployments on cloud platforms.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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 an Intermediate Agentic AI Engineer, you will serve as the technical backbone for building and scaling our production-grade multi-agent platform. Working primarily with Python and modern orchestration frameworks like LangGraph, you will design a hierarchical multi-agent layer, implement dynamic context-loading mechanisms, and integrate standardized tool-calling interfaces to transition legacy workflows into fully autonomous systems.
This role is critical to optimizing our AI ecosystem's performance, token economics, and operational security. By establishing automated evaluation harnesses, robust observability stacks, and enterprise-grade security guardrails, your work will directly drive the reliability, safety, and scalable impact of high-performing AI agents across our organization.
Key Responsibilities
-
Design and deploy a hierarchical multi-agent workflow using LangGraph, successfully transitioning legacy single-prompt implementations into modular agentic systems.
-
Build domain-specific sub-agents featuring dynamic, on-demand context loading to significantly optimize latency and token economics.
-
Design and integrate scalable tool-calling capabilities and standardized connectors using the Model Context Protocol (MCP).
-
Optimize LLM observability, reasoning traces, and cost-tracking systems by setting up and managing LangFuse.
-
Lead the establishment of automated evaluation harnesses and regression testing suites using tools like PromptFlow or PromptFoo against benchmark datasets.
-
Collaborate on implementing strict security guardrails and access controls following OWASP Top 10 standards for LLM applications.
Requirements
-
5+ years of professional software development experience, with a primary focus on Python.
-
2+ years of hands-on experience building production AI agents or complex LLM workflows using LangGraph or LangChain.
-
Tech Stack: Strong practical experience with modern foundation models (Anthropic Claude, OpenAI, Google Gemini), open-weights models, and advanced prompt engineering techniques.
-
Observability & Eval: Hands-on experience with LLM observability platforms (e.g., LangFuse) and automated evaluation frameworks.
-
Infrastructure: Familiarity with containerized cloud environments including Azure and Docker.
-
Ecosystem: Hands-on experience with the Microsoft 365 Agents SDK.
-
Soft Skills: High ownership mindset, strong problem-solving initiative, and an empathetic, collaborative team approach.
-
Language: Advanced English (written and spoken) is mandatory.
Nice to Have
-
Experience working with Retrieval-Augmented Generation (RAG) pipelines and vector database integrations (e.g., Pinecone, Weaviate, Qdrant).
-
Familiarity with enterprise AI security frameworks, data privacy compliance, and prompt injection mitigation strategies.
-
Exposure to serverless architectures and microservices deployments on cloud platforms.
What You’ll Love
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100% Remote work environment.
-
Holidays off matching local calendar standards.
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Generous Paid Time Off (PTO).
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Health insurance assistance.
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Competitive USD compensation.
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Clear growth opportunities and continuous learning support.
Company
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