Generative AI Engineer

The Phoenix Group®; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

The Phoenix Group® is seeking an experienced AI Engineer to design and build agentic AI applications capable of reasoning, planning, and executing tasks while interacting with external tools and systems.

You will develop both single-agent and multi-agent architectures for sophisticated business workflows, create tool-calling capabilities, and optimize RAG pipelines with vector databases. Join a cross-functional team to deploy and monitor production AI solutions in cloud environments.

Qualifications

• Strong Python development experience and software engineering fundamentals.
• Hands-on experience developing Generative AI or LLM-powered applications.
• Experience building AI agents or agentic workflows, beyond standalone chatbots or prompt-based applications.
• Experience with LLM APIs, function/tool calling, structured outputs, and model orchestration.
• Understanding of RAG, embeddings, vector databases, semantic search, and context management.
• Experience integrating AI applications with APIs, databases, SaaS platforms, or enterprise systems.
• Familiarity with agent frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies.
• Experience deploying AI applications in Azure, AWS, or GCP.
• Understanding of AI evaluation, observability, security, and guardrails.

Responsibilities

• Design and build agentic AI applications that can reason, plan, execute tasks, and interact with external tools and systems.
• Develop single-agent and multi-agent architectures for complex business workflows.
• Build tool-calling capabilities that allow agents to interact with APIs, databases, enterprise applications, and internal services.
• Develop and optimize RAG pipelines, vector search, embeddings, and contextual retrieval.
• Integrate LLMs from platforms such as OpenAI, Anthropic, Google, and open-source providers.
• Build orchestration workflows using LangGraph, LangChain, Semantic Kernel, AutoGen, or similar technologies.
• Implement memory, state management, guardrails, human-in-the-loop controls, and structured outputs.
• Develop evaluation frameworks to measure agent accuracy, reliability, latency, and task completion.
• Deploy, monitor, and improve AI applications in production environments.
• Partner with engineering, data, security, product, and business teams to identify opportunities for agent-driven automation.

Skills

Python development

Generative AI/LLM

Agentic workflows

LLM APIs and tool calling

RAG and vector databases

Cloud deployments (Azure/AWS/GCP)

Security & observability

Tools

LangGraph

LangChain

Semantic Kernel

AutoGen

CrewAI

Pinecone

Weaviate

Qdrant

Milvus

pgvector

Job description

• Design and build agentic AI applications that can reason, plan, execute tasks, and interact with external tools and systems.


• Develop single-agent and multi-agent architectures for complex business workflows.


• Build tool-calling capabilities that allow agents to interact with APIs, databases, enterprise applications, and internal services.


• Develop and optimize RAG pipelines, vector search, embeddings, and contextual retrieval.


• Integrate LLMs from platforms such as OpenAI, Anthropic, Google, and open-source providers.


• Build orchestration workflows using LangGraph, LangChain, Semantic Kernel, AutoGen, or similar technologies.


• Implement memory, state management, guardrails, human-in-the-loop controls, and structured outputs.


• Develop evaluation frameworks to measure agent accuracy, reliability, latency, and task completion.


• Deploy, monitor, and improve AI applications in production environments.


• Partner with engineering, data, security, product, and business teams to identify opportunities for agent-driven automation.

Responsibilities

• Design and build agentic AI applications that can reason, plan, execute tasks, and interact with external tools and systems.


• Develop single-agent and multi-agent architectures for complex business workflows.


• Build tool-calling capabilities that allow agents to interact with APIs, databases, enterprise applications, and internal services.


• Develop and optimize RAG pipelines, vector search, embeddings, and contextual retrieval.


• Integrate LLMs from platforms such as OpenAI, Anthropic, Google, and open-source providers.


• Build orchestration workflows using LangGraph, LangChain, Semantic Kernel, AutoGen, or similar technologies.


• Implement memory, state management, guardrails, human-in-the-loop controls, and structured outputs.


• Develop evaluation frameworks to measure agent accuracy, reliability, latency, and task completion.


• Deploy, monitor, and improve AI applications in production environments.


• Partner with engineering, data, security, product, and business teams to identify opportunities for agent-driven automation.

Qualifications

• Strong Python development experience and software engineering fundamentals.


• Hands-on experience developing Generative AI or LLM-powered applications.


• Experience building AI agents or agentic workflows, beyond standalone chatbots or prompt-based applications.


• Experience with LLM APIs, function/tool calling, structured outputs, and model orchestration.


• Understanding of RAG, embeddings, vector databases, semantic search, and context management.


• Experience integrating AI applications with APIs, databases, SaaS platforms, or enterprise systems.


• Familiarity with agent frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies.


• Experience deploying AI applications in Azure, AWS, or GCP.


• Understanding of AI evaluation, observability, security, and guardrails.

Preferred

• Experience designing multi-agent systems.


• Experience with Model Context Protocol (MCP).


• Experience building agents that automate end-to-end business processes.


• Knowledge of LLMOps/MLOps and production monitoring.


• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.


• Experience working with enterprise data, security, permissions, and identity controls.