GenAI Engineer: Agentic AI, RAG, & Knowledge Graphs

Jobtailor

Town of Florida (NY)

On-site

USD 130,000 - 195,000

Full time

4 days ago

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Job summary

Jobtailor in the United States (New York) seeks an experienced AI/GenAI software engineer to build and integrate generative AI applications, design context engineering workflows, and advance retrieval-augmented generation systems.

You will collaborate with data scientists to preprocess data, develop APIs, and contribute to knowledge graphs and agentic workflows. Cloud exposure and strong Python skills are essential for production deployment and governance.

Qualifications

3–5 years of professional experience in software or AI development.
Hands-on exposure to Generative AI and agentic AI.
Demonstrated experience delivering AI/GenAI projects in a collaborative team environment.
Exposure to cloud platforms and services.
Proficiency in Python for GenAI development, data preprocessing, and scripting.
Solid understanding of foundation models, LLMs, tokenization, embeddings, and context windows.
Hands-on experience with prompt engineering and context engineering techniques.
Experience building RAG systems, including chunking strategies, vector databases, and hybrid search techniques.
Familiarity with knowledge graphs and an interest in Graph RAG for relationship-aware, multi-hop retrieval.
Exposure to agentic AI development using Google ADK, LangGraph, CrewAI, or the OpenAI Agents SDK.
Working knowledge of tool/function calling and the Model Context Protocol (MCP); awareness of the A2A protocol.
Understanding of agent harness basics, including session/state management, memory, guardrails, and execution controls.
Practical experience consuming GenAI APIs such as OpenAI, Gemini, and Claude, and orchestration frameworks such as LangChain and LlamaIndex.
Understanding of application deployment and containerization using Docker.
Understanding of version control systems using Git.
Awareness of AI compliance, data privacy, guardrails, and responsible AI principles.
Strong teamwork and communication abilities.
Willingness to learn new AI/GenAI and agentic technologies and frameworks.
Analytical mindset and attention to detail.
Openness to feedback and continuous improvement.
Bachelor's degree or Master's degree in Computer Science, Data Science, AI, or a related field.

Responsibilities

Build and integrate generative AI applications using pre-trained and hosted foundation models.
Design and implement context engineering workflows using system instructions, retrieved knowledge, tool definitions, conversation memory, and task metadata.
Contribute to prompt engineering and AI-powered workflows.
Develop and maintain Retrieval-Augmented Generation (RAG) systems.
Assist with building knowledge graphs and Graph RAG pipelines.
Contribute to agentic workflows using tool-calling, structured planning, and memory.
Integrate agents with external tools and data sources via MCP and enable agent-to-agent collaboration via A2A.
Assist with deployment, monitoring, and maintenance of GenAI and agentic applications in production.
Collaborate with data scientists and engineers to integrate AI capabilities.
Perform data preprocessing, document ingestion, and API development.
Participate in code reviews, testing, and documentation.
Stay updated with advancements in GenAI and agentic AI and share relevant learnings.