Artificial Intelligence Engineer

360 Technology; Dallas (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

360 Technology seeks an experienced Agentic AI Engineer to build autonomous, goal-oriented AI solutions for banking. You will design agentic workflows, develop AI agents that can use tools and APIs, and integrate LLM-powered solutions with enterprise banking systems.

The role requires strong software engineering skills, production-grade Python, and expertise in agent orchestration frameworks, RAG pipelines, and secure banking deployments.

Qualifications

• Strong hands-on programming experience in Python and enterprise software development.
• 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.

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 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.

Skills

Python

Agentic AI

LLMs

LangChain/LangGraph

RAG pipelines

REST APIs integration

Cloud platforms (AWS/Azure/GCP)

Banking security/compliance

Observability & testing

Multi-agent orchestration

Education

BS in CS/Engineering

Advanced AI/ML degree

Tools

LangGraph

LangChain

Vector databases

Embeddings

Docker

CI/CD

Job description

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.