Senior Agentic AI Engineer

Cynet SystemsReston, VA

About The Position

We are seeking a Senior Agentic AI Engineer with a strong software engineering background and hands-on experience in Generative AI, LLM applications, and Agentic AI solutions. The role involves building and deploying production-grade cloud applications, focusing on creating LLM-powered applications for various use cases including text generation, summarization, Q&A, conversational AI, enterprise knowledge search, and multi-agent orchestration. You will develop advanced RAG pipelines, build secure integrations between AI agents and enterprise tools, and design user-facing AI application experiences. This position requires expertise in agentic frameworks, evaluation of agent quality, and LLMOps, working within an enterprise environment with large-scale, secure AI deployments.

Requirements

  • Bachelor's degree (or international equivalent) and 8+ years of relevant software engineering experience.
  • 2-3 years of hands-on Generative AI, LLM application, or Agentic AI solution development experience.
  • Strong software engineering background with experience designing and deploying production-grade cloud applications.
  • Experience building front-end applications with React and TypeScript, and back-end services with Node.js or comparable application frameworks.
  • Hands-on experience building Generative AI and RAG applications with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector or hybrid search, knowledge retrieval, grounding, and citations.
  • Experience with Agentic AI frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or comparable orchestration frameworks; including single-agent and multi-agent systems, tool-calling workflows, and human-in-the-loop controls.
  • Experience evaluating and improving agent quality, including prompt engineering, test datasets, LLM-based evaluation, safety checks, and production feedback loops.
  • Strong knowledge of LLMOps, CI/CD, containerization (Docker and Kubernetes), observability, and production operations for AI applications.
  • Good understanding of RESTful API principles, asynchronous application patterns, secure integrations, relational databases, SQL, and data-access patterns; familiarity with SQL/NoSQL data stores and data engineering or ETL pipelines.
  • Experience working in an enterprise environment with large-scale, secure AI deployments, including identity, authorization, data privacy, compliance, and production monitoring.
  • Strong analytical, problem-solving, collaboration, and communication skills.

Responsibilities

  • Build LLM-powered applications for text generation, summarization, Q&A, conversational AI, enterprise knowledge search, and multi-agent orchestration.
  • Develop advanced RAG pipelines using embeddings, Azure AI Search vector and hybrid retrieval, document chunking, metadata filtering, reranking, citations, and grounding techniques with enterprise data.
  • Build secure, reliable integrations between AI agents and enterprise tools, REST APIs, relational databases, and event-driven services.
  • Develop and maintain user-facing AI application experiences using React and TypeScript, and supporting application services using Node.js or comparable back-end technologies.
  • Design and implement single-agent and multi-agent systems for intelligent automation, decisioning, and complex workflows.
  • Build autonomous and human-in-the-loop agents that plan, reason, act, and interact with tools, APIs, enterprise data, and event-driven systems.
  • Develop agentic workflows using Microsoft Agent Framework, Azure AI Foundry services, or comparable modern orchestration frameworks.
  • Implement configuration-driven agent behavior, prompt and tool management, authorization boundaries, and resilient error-handling patterns.
  • Define and automate evaluation approaches for agent quality, including groundedness, relevance, citation quality, safety, and regression testing.
  • Instrument agent workflows for traces, tool calls, latency, token usage, errors, and operational metrics using OpenTelemetry, Application Insights, or comparable observability platforms.
  • Build highly scalable, secure, containerized solutions with CI/CD, health checks, horizontal scaling, and production monitoring.
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