Sr Applied AI Engineer

Judge Group, Inc.; Dallas (TX); On-site; USD 124,000 - 131,000; Full time

Benefits offered by this job

Travel up to 50%

Medical Insurance

Dental Insurance

Vision Insurance

Job summary

Judge Group, Inc. is seeking a Sr Applied AI Engineer to lead production-grade AI initiatives at customer sites.

You will transform rapid conversational prototypes into scalable agentic solutions, owning end-to-end engineering, security, and deployment in enterprise environments. You will architect flows for Gemini-powered CX and related AI components, build Eval pipelines, and collaborate with customer teams to ship measurable ROI while advancing state management and supervision in large-scale

Qualifications

• Bachelor’s degree in Engineering, CS or equivalent.
• 5 years of software development experience with Python.
• Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
• Experience deploying resources via Terraform to automate the setup of agents and networking.
• Experience building pipelines for structured and unstructured data using vector databases and RAG-like architectures.

Responsibilities

• Lead development for complex Conversational AI and CX applications, transitioning prototypes to production-grade workflows.
• Architect and code conversational flows optimized to connect AI products with customers’ live infrastructure and security perimeters.
• Build high-performance evaluation pipelines and observability for agentic workloads, focusing on latency, accuracy, and safety.
• Identify friction points in the AI stack and convert them into reusable modules or product feature requests.
• Co-build with customer engineering teams to ensure long-term project success and adoption.

Skills

Python

Cloud platforms

Terraform

Vector databases

RAG architectures

Full-stack

Technical leadership

Education

Bachelor's degree in Engineering or Computer Science

Master’s or PhD in AI/CS (preferred)

Tools

LangGraph

CrewAI

ADK

Job description

Location: Dallas, TX Salary: $90.00 USD Hourly – $95.00 USD Hourly Description: Our client is currently seeking a Sr Applied AI Engineer

As a Senior Applied AI Engineer, you are the “Agent Engineer” and primary driver for our customers’ most critical AI initiatives. You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering lifecycle – from “art of the possible” prototyping to real-world business value and scalable, secure AI systems. This is a high-impact role focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites. The role requires a deep understanding of software engineering, Machine Learning Operations, and cloud infrastructure.

You will function as an embedded builder who bridges the gap between frontier AI products and production-grade reality – moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. This role is designed for high-agency engineers with a founder’s mindset who can solve integration complexity, data readiness, and state-management challenges that block AI from reaching enterprise-grade maturity, while feeding real-world field insights back into the product roadmap.

Job responsibilities

• Serve as lead developer for complex Conversational AI and CX applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.
• Architect and code conversational flows that are not just functional, but optimized for the “connective tissue” between Conversational AI products (Gemini-powered Conversational Agents/CX, Customer Engagement Suite (CES), and Contact Center AI (CCAI)) and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.
• Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize complex agentic workloads – focusing on reasoning loops, tool selection, latency, accuracy, and safety – while maintaining production-grade security and networking.
• Identify repeatable field patterns and technical “friction points” in the AI stack, converting them into reusable modules or formal product feature requests for engineering teams.
• Co-build with customer engineering teams to instill strong development best practices, ensuring long-term project success and high end-user adoption.

Qualifications for success

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with software development using Python or similar coding languages.
• Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
• Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, or networking.
• Experience building pipelines for structured and unstructured data using vector databases and RAG-like architectures to power enterprise AI solutions.
• Experience building full-stack applications that interact with enterprise IT infrastructures, and taking production-grade, customer-facing AI solutions from conception to launch.
• Experience leading technical discovery sessions with customers.

MUST HAVE

• Hands-on experience implementing and customizing Google Conversational AI product suite, including Conversational Agents (Gemini-powered CX), Customer Engagement Suite (CES), and Contact Center AI (CCAI).

Preferred qualifications

• Master’s or PhD in AI, Computer Science, or a related technical field.
• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
• Experience debugging agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real time.
• Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
• Knowledge of “LLM-native” metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
• Track record of troubleshooting live, high-traffic systems during critical windows.
• Ability to travel up to 50% of the time.
• Medical, dental, and vision insurance are available to qualified candidates who meet eligibility requirements.