Agentic AI Developer

LBMC; Vaughan; Remote; CAD 140,000 - 200,000; Full time

Job summary

A leading company is seeking a Mid-Senior Agentic AI Developer to lead the development of AI autonomous agents. This role involves hands-on engineering, client collaboration, and utilizing advanced frameworks like LangChain and LangGraph. Ideal candidates will have strong Python skills and experience in RAG techniques. The position offers flexibility to work remotely, with a preference for candidates near Nashville, TN.

Benefits

Medical insurance

Vision insurance

401(k)

Paid paternity leave

Paid maternity leave

Qualifications

• Experience building autonomous AI agents or complex workflows.
• Strong Python programming skills.
• Expertise in Retrieval-Augmented Generation (RAG) and information retrieval methods.

Responsibilities

• Design, develop, and deploy autonomous AI agents.
• Orchestrate complex LLM workflows using frameworks.
• Collaborate closely with clients and cross-functional teams.

Skills

Python

Communication

RAG

Graph RAG

Tools

LangChain

LangGraph

MCP

Job description

As a Mid-Senior Agentic AI Developer on our AI team, you will lead the hands-on development of AI autonomous agents that drive solutions for clients. This is a practical engineering role (not just research) focused on building and deploying real-agentic AI systems that deliver value for client operations. The position offers flexibility to work remotely, with a preference for candidates based in or near Nashville, TN (for occasional in-person collaboration). You should be comfortable in a consulting environment – interacting directly with clients to gather requirements, demonstrate solutions, and ensure successful implementation.

In this role, you will focus on the end-to-end nature of solution development, from concept to production leveraging state-of-the-art frameworks and techniques to create AI-driven agents. You will orchestrate complex LLM-powered workflows using frameworks like LangChain and LangGraph. You will implement RAG pipelines to augment the agents' knowledge with enterprise data, including Graph RAG approaches that integrate knowledge graphs. Additionally, you will utilize emerging standards like the Model Context Protocol (MCP) to connect AI agents with live data sources and tools in a secure, scalable manner for enterprise systems. We are looking for a developer who can combine deep technical expertise with creativity and client-focused insight to lead complex AI implementations from concept to production.

SCOPE OF WORK

• Design, develop , and deploy autonomous AI agents that solve real-world problems for clients in healthcare, utilities, finance, private equity, and other industries. This includes end-to-end solution development from initial prototype to production deployment.
• Orchestrate complex LLM workflows using frameworks such as LangChain and LangGraph to build controllable, multi-step AI agent processes. Develop agents capable of tool use, memory, and reasoning across tasks (e.g. data analysis, report generation, decision support).
• Implement Retrieval-Augmented Generation (RAG) techniques to integrate external knowledge into agent responses. Leverage Graph RAG methods to incorporate knowledge graph relationships for deeper context when appropriate.
• Utilize the Model Context Protocol (MCP) (or similar frameworks) to connect agents with client data sources and systems. Set up secure, two-way integrations that allow AI agents to pull in real-time information (e.g. from internal databases or enterprise apps) and to take actions or update data as needed.
• Stay up-to-date with advancements in AI and agent frameworks. Proactively research and experiment with new models (e.g. OpenAI/Claude updates), libraries, and techniques (such as emerging agent orchestration tools or memory architectures) to continually enhance LBMC's offerings.
• Collaborate closely with clients and cross-functional teams. Work with consultants, domain experts, and client stakeholders to gather requirements, understand business workflows, and tailor AI solutions to client needs. Translate client requests into technical specifications and explain complex AI concepts in clear, client-friendly terms.
• Test, evaluate, and refine agent performance through iterative development. Monitor agents' outputs, debug and resolve errors or unexpected behaviors, optimize prompts and retrieval methods, and ensure the AI solutions meet quality, accuracy, and reliability standards. Implement evaluation metrics and logging to continuously improve agent results.
• Mentor and provide guidance to junior developers or team members as needed. As a mid-senior professional, you may lead code reviews, share best practices in Python and AI development, and contribute to a knowledge-sharing culture within the AI practice.

IDEAL CANDIDATE PROFILE

Required Skills

• Experience building autonomous AI agents or complex workflows using frameworks like LangChain or LangGraph (or similar agent orchestration frameworks). You should be comfortable with the concepts of agent loops, tool usage, multi-step reasoning, and state management in these frameworks.
• Strong Python programming skills – able to write clean, efficient, and well-documented code. Experience structuring Python projects, handling data processing, and integrating with APIs or databases.
• Expertise in Retrieval-Augmented Generation (RAG) and information retrieval methods. Ability to set up document or knowledge retrieval systems (e.g. vector databases, semantic search indexes) to feed context into LLMs.
• Knowledge of Graph RAG and knowledge graph integration is a strong plus. Understanding how to utilize knowledge graphs or ontologies alongside text embeddings to enrich model context (for example, linking entities and relationships to improve answer quality).
• Familiarity with Model Context Protocol (MCP) and similar approaches for connecting AI models to external data/tools. Experience implementing or using APIs, connectors, or middleware that allow AI agents to securely access databases, files, or enterprise applications.
• Excellent communication skills, both written and verbal. Proven ability to explain technical concepts (AI models, data pipelines, etc.) to non-technical stakeholders and to document designs and processes clearly. Comfort in client-facing discussions is essential.

Preferred Skills and Attributes

• Familiarity with additional AI agent frameworks or tools – for example, experience experimenting with AutoGPT, BabyAGI, ReAct agents, OpenAI Functions, or other multi-agent systems. A broad awareness of the agentic AI ecosystem demonstrates passion and initiative.
• Experience or proficiency with knowledge graph and vector database technologies – such as Neo4j or RDF graph databases for knowledge graphs, and Pinecone, FAISS, or Elasticsearch for vector-based document retrieval. This experience will aid in implementing advanced RAG and GraphRAG systems
• Cloud and DevOps experience – knowledge of deploying AI/LLM solutions on cloud platforms (AWS, Azure, GCP). Experience with containerization (Docker), serverless functions, or orchestration tools for scaling AI services is a plus.
• Contributions to AI/ML communities or open-source projects – a history of contributing to relevant open source (e.g. LangChain plugins) or research publications/blogs shows a drive to stay at the forefront of the field.
• Located in or near Nasvhille, TN – or willingness to travel occasionally is a plus. Being in proximity to our Nashville headquarters can facilitate face-to-face teamwork and client meetings.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Consulting

• Industries

  Business Consulting and Services

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Inferred from the description for this job

Medical insurance

Vision insurance

401(k)

Paid paternity leave

Paid maternity leave

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