Gen AI / Agentic AI Developer

TL;DR

Gen AI / Agentic AI Developer (Python/LLM/RAG): Building enterprise LLM-powered applications, retrieval-augmented generation solutions, and agentic AI workflows with an accent on multi-agent orchestration, backend APIs, and production deployment. Focus on designing complex retrieval pipelines, integrating agents with enterprise systems, and implementing LLMOps for evaluation, monitoring, and cost control.

Location: Day-one onsite hybrid work in New York City, NY or Charlotte, NC; candidates must be based in or willing to travel to Chicago, New York City, Atlanta, or Charlotte for an in-person interview.

Salary: $80,420–$106,050 per year, with potential additional variable compensation, bonuses, or commissions.

Company

hirify.global is a global business and technology transformation partner delivering strategy, design, engineering, AI, cloud, and data solutions for enterprises.

What you will do

• Build enterprise GenAI applications using LLMs, RAG, agents, and tool-calling workflows.
• Design multi-agent architectures including planner, retriever, executor, validator, and human-in-the-loop agents.
• Develop Python backend APIs and microservices with FastAPI, Flask, and REST.
• Integrate AI agents with enterprise systems, databases, APIs, document repositories, and cloud services.
• Implement document ingestion, embeddings, vector search, reranking, and retrieval pipelines.
• Deploy and monitor applications with Docker, Kubernetes, CI/CD, and LLMOps practices.

Requirements

• Strong hands-on Python development experience.
• Experience with OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Gemini, Llama, or Mistral.
• Hands-on experience with at least one agentic framework such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or LlamaIndex.
• Understanding of RAG, embeddings, vector databases, semantic search, prompt engineering, REST APIs, cloud deployment, Docker, and CI/CD.
• Ability to work with structured and unstructured data from documents, APIs, databases, and knowledge bases.
• Ability to explain an end-to-end GenAI or agentic AI project, including architecture, deployment, evaluation, and business impact.

Nice to have

• Experience with multi-agent orchestration, tool calling, memory, planning, reflection, and evaluation.
• Exposure to MCP, Graph RAG, Neo4j, knowledge graphs, or entity extraction.
• Knowledge of LLMOps tools, including LangSmith, MLflow, Phoenix, Ragas, TruLens, Arize, or OpenTelemetry.
• Experience with AWS Bedrock/SageMaker, Azure OpenAI/AI Search, or GCP Vertex AI.
• Understanding of AI guardrails, prompt injection prevention, PII masking, access control, and responsible AI.

Culture & Benefits

• Flexible work environment with a collaborative global community.
• Medical, dental, vision, mental health, and well-being programs.
• Paid time off, company holidays, and paid parental leave.
• 401(k), employee share ownership, life and disability insurance, and financial well-being programs.
• Mentoring, coaching, learning programs, employee resource groups, and family support benefits.

Hiring process

• The interview process includes an in-person round in Chicago, New York City, Atlanta, or Charlotte.
• Candidates may be asked to discuss an end-to-end GenAI or agentic AI project in detail.