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Artificial Intelligence Engineer
360 Technology
3 hours ago
Full-time
On-site
Dallas, Texas, United States
We are seeking an experienced Agentic AI Engineer with strong hands-on expertise in Python, Generative AI, Large Language Models (LLMs), and AI agent development to build intelligent, autonomous, and goal-oriented AI solutions for banking applications.
The ideal candidate will have experience designing and implementing agentic workflows, developing AI agents that can use tools and APIs, and integrating LLM-powered solutions with enterprise banking systems. Candidates must possess strong software engineering skills and the ability to build secure, scalable, and production-ready AI applications in a regulated financial services environment.
Key Responsibilities
Design, develop, and deploy AI agents capable of reasoning, planning, tool execution, and multi-step task orchestration.
Build multi-agent systems and agentic workflows using frameworks such as LangGraph, LangChain, or Microsoft AutoGen.
Develop production-grade AI applications using Python, LLMs, prompt engineering, and structured output generation.
Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources, vector databases, and embedding models.
Integrate AI agents with REST APIs, enterprise applications, databases, and banking platforms.
Implement tool calling, workflow orchestration, memory management, and human-in-the-loop approval mechanisms.
Develop automated evaluation, monitoring, tracing, and testing frameworks to ensure AI solution reliability.
Apply guardrails to mitigate hallucinations, prompt injection, unauthorized tool usage, and inappropriate data exposure.
Ensure compliance with banking security, data privacy, auditability, and responsible AI requirements.
Collaborate with banking stakeholders, architects, data engineers, and application development teams to deliver business-focused AI solutions.
Optimize agent performance, response accuracy, latency, scalability, and operational cost.
Required Technical Skills
Strong hands-on programming experience in
Python
and enterprise software development.
Practical experience developing
Agentic AI applications, AI agents, and multi-step autonomous workflows .
Strong knowledge of LLMs, Generative AI, prompt engineering, function calling, and structured outputs.
Hands-on experience with
LangGraph, LangChain, or equivalent agent orchestration frameworks .
Experience implementing RAG pipelines, embeddings, vector databases, and semantic search.
Experience integrating AI solutions with REST APIs, enterprise systems, and databases.Knowledge of agent evaluation, observability, testing, error handling, and performance optimization.
Familiarity with cloud-based AI services and deployment platforms such as AWS, Azure, or Google Cloud.
Understanding of AI security, access controls, responsible AI, and enterprise data governance.
Banking Domain Requirements
Experience delivering AI, ML, or GenAI solutions within banking or financial services.
Understanding of banking workflows such as customer service, payments, lending, fraud detection, risk management, compliance, or operations.
Knowledge of sensitive financial data handling, regulatory controls, audit trails, and secure AI implementation.
Ability to develop AI solutions that operate within controlled banking environments and comply with enterprise security standards.
Preferred Qualifications
Experience with multi-agent orchestration, planning, state management, and agent-to-agent communication.
Exposure to Model Context Protocol (MCP), enterprise tool integrations, and AI workflow automation.
Experience with LLMOps, CI/CD, containerization, and production deployment of AI applications.
Familiarity with AI governance, model evaluation, observability, and human approval workflows.
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The ideal candidate will have experience designing and implementing agentic workflows, developing AI agents that can use tools and APIs, and integrating LLM-powered solutions with enterprise banking systems. Candidates must possess strong software engineering skills and the ability to build secure, scalable, and production-ready AI applications in a regulated financial services environment.
Key Responsibilities
Design, develop, and deploy AI agents capable of reasoning, planning, tool execution, and multi-step task orchestration.
Build multi-agent systems and agentic workflows using frameworks such as LangGraph, LangChain, or Microsoft AutoGen.
Develop production-grade AI applications using Python, LLMs, prompt engineering, and structured output generation.
Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources, vector databases, and embedding models.
Integrate AI agents with REST APIs, enterprise applications, databases, and banking platforms.
Implement tool calling, workflow orchestration, memory management, and human-in-the-loop approval mechanisms.
Develop automated evaluation, monitoring, tracing, and testing frameworks to ensure AI solution reliability.
Apply guardrails to mitigate hallucinations, prompt injection, unauthorized tool usage, and inappropriate data exposure.
Ensure compliance with banking security, data privacy, auditability, and responsible AI requirements.
Collaborate with banking stakeholders, architects, data engineers, and application development teams to deliver business-focused AI solutions.
Optimize agent performance, response accuracy, latency, scalability, and operational cost.
Required Technical Skills
Strong hands-on programming experience in
Python
and enterprise software development.
Practical experience developing
Agentic AI applications, AI agents, and multi-step autonomous workflows .
Strong knowledge of LLMs, Generative AI, prompt engineering, function calling, and structured outputs.
Hands-on experience with
LangGraph, LangChain, or equivalent agent orchestration frameworks .
Experience implementing RAG pipelines, embeddings, vector databases, and semantic search.
Experience integrating AI solutions with REST APIs, enterprise systems, and databases.Knowledge of agent evaluation, observability, testing, error handling, and performance optimization.
Familiarity with cloud-based AI services and deployment platforms such as AWS, Azure, or Google Cloud.
Understanding of AI security, access controls, responsible AI, and enterprise data governance.
Banking Domain Requirements
Experience delivering AI, ML, or GenAI solutions within banking or financial services.
Understanding of banking workflows such as customer service, payments, lending, fraud detection, risk management, compliance, or operations.
Knowledge of sensitive financial data handling, regulatory controls, audit trails, and secure AI implementation.
Ability to develop AI solutions that operate within controlled banking environments and comply with enterprise security standards.
Preferred Qualifications
Experience with multi-agent orchestration, planning, state management, and agent-to-agent communication.
Exposure to Model Context Protocol (MCP), enterprise tool integrations, and AI workflow automation.
Experience with LLMOps, CI/CD, containerization, and production deployment of AI applications.
Familiarity with AI governance, model evaluation, observability, and human approval workflows.
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