Agentic AI Engineer- Audit Data Analytics

Tricascade Tech; Texas; On-site; USD 140,000 - 180,000; Full time

Job summary

Tricascade Tech is seeking an Agentic AI Engineer for Data Analytics in Dallas to design and deploy multi-agent AI systems for enterprise audit. The role focuses on building GenAI pipelines, data analytics workflows, and governance practices, with 5–7 years of software engineering experience required.

You will work onsite five days a week, integrating AI with enterprise data, REST APIs, and cloud services while ensuring security, compliance, and auditable reporting across audit processes.

Qualifications

• 5–7 years of software engineering experience, including current hands-on development of production GenAI and agentic AI solutions.
• Strong Python programming skills, including OOP, design patterns, API development, and CI/CD practices.
• Experience designing and implementing agentic AI workflows using LangGraph, CrewAI, AutoGen, Google ADK, or similar frameworks.
• Hands-on experience with LLM orchestration, LangChain, Retrieval-Augmented Generation (RAG), prompt engineering, and multi-agent architectures.
• Experience with vector databases, embeddings, semantic search, and integrating structured and unstructured enterprise data.
• Strong SQL and data analytics skills, including data extraction, transformation, analysis, and anomaly detection.
• Experience integrating AI solutions with enterprise applications, REST APIs, databases, and workflow automation platforms.

Responsibilities

• Design and build agentic AI systems using frameworks such as LangGraph, CrewAI, AutoGen, or Google ADK.
• Develop RAG pipelines grounded in regulatory guidance, internal policies, and enterprise documents.
• Automate workflows using agents that securely query data, analyze unstructured content, and generate reports.
• Analyze transactional and operational data to identify anomalies, discrepancies, and risks.
• Implement AI governance, security guardrails, access controls, PII protection, and auditability.
• Provide technical guidance and establish LLM evaluation and monitoring practices.

Skills

Python

OOP

CI/CD

API development

LangGraph

LangChain

RAG pipelines

LLM orchestration

Tools

LangGraph

CrewAI

AutoGen

Google ADK

LangChain

RAGAS

PromptBench

Job description

Role - Agentic AI Engineer- Audit Data Analytics

Location - Dallas, TX (5 days onsite)

Duration - 6+ months

We are seeking a Agentic AI Engineer for Data Analytics with 5–7 years of software engineering experience to build GenAI and multi-agent solutions for enterprise internal audit. The role combines AI engineering, enterprise data analytics, and workflow automation, including document analysis, compliance checks, anomaly detection, and reporting. This position requires being onsite 5 days a week.

Responsibilities:

• Design and build agentic AI systems using frameworks such as LangGraph, CrewAI, AutoGen, or Google ADK
• Develop RAG pipelines grounded in regulatory guidance, internal policies, and enterprise documents
• Automate workflows using agents that securely query data, analyze unstructured content, and generate reports
• Analyze transactional and operational data to identify anomalies, discrepancies, and risks
• Implement AI governance, security guardrails, access controls, PII protection, and auditability
• Provide technical guidance and establish LLM evaluation and monitoring practices

Requirements:

• 5–7 years of software engineering experience, including current hands-on development of production GenAI and agentic AI solutions
• Strong Python programming skills, including object-oriented programming (OOP), design patterns, API development, and CI/CD practices
• Experienced designing and implementing agentic AI workflows using LangGraph, CrewAI, AutoGen, Google ADK, or similar frameworks
• Hands-on experience with LLM orchestration, LangChain, Retrieval-Augmented Generation (RAG), prompt engineering, and multi-agent architectures
• Experience with vector databases, embeddings, semantic search, and integrating structured and unstructured enterprise data
• Strong SQL and data analytics skills, including data extraction, transformation, analysis, and anomaly detection
• Experience integrating AI solutions with enterprise applications, REST APIs, databases, and workflow automation platforms
• Familiarity with LLM evaluation, monitoring, and observability tools such as RAGAS, PromptBench, or similar frameworks
• Understanding of AI security, governance, access controls, PII protection, and auditability within enterprise environments
• Experience working with cloud platforms such as Azure, AWS, or GCP to develop and deploy scalable AI solutions
• Understanding of risk management, regulatory compliance, financial operations, or enterprise audit processes
• Strong problem-solving and communication skills with the ability to translate business requirements into practical AI and data analytics solutions