Sr Applied AI Engineer

Stellent IT LLC; Dallas (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

Stellent IT LLC is seeking a Sr Applied AI Engineer in Dallas, TX. The role is onsite at the client and Tech Mahindra office for a 12+ month contract. You will lead the delivery of production-grade agentic AI solutions, bridging frontier AI products with enterprise deployment.

You will own end-to-end engineering lifecycle, from prototyping to scalable, secure AI systems, collaborating with customer teams to implement robust CUJs and integrations.

Qualifications

• Bachelor's degree in Engineering, CS, or equivalent practical experience.
• 5 years of software development experience with Python.
• Experience architecting AI systems on cloud platforms (GCP).
• Experience deploying resources via Terraform or similar tools.
• Experience building pipelines for structured and unstructured data using vector databases and RAG architectures.
• Experience leading technical discovery sessions with customers.
• MUST HAVE: Hands-on experience implementing and customizing Google Conversational AI product suite (Gemini CX, CES, CCAI).

Responsibilities

• Serve as lead developer for complex Conversational AI and CX applications, moving prototypes to production-grade workflows.
• Architect and code conversational flows connecting Gemini CX, CES, CCAI with customers' live infrastructure.
• Build high-performance Eval pipelines and observability frameworks for complex agent workloads.
• Identify repeatable field patterns and convert them into reusable modules or feature requests.
• Co-build with customer engineering teams to ensure long-term project success and adoption.

Skills

Python

AI systems design

GCP

Terraform

Vector databases

RAG architectures

Full-stack integration

Customer discovery

Google CCAI

Education

Bachelor's degree in Engineering/CS

Tools

Terraform

APIs & integration

Job description

Sr Applied AI Engineer

Dallas TX

12 + Month contract

Must have Google CCAI

JD below:

NOTE: Candidate 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).

Senior Applied AI Engineer Conversational & Agentic Systems (Gemini CX, CES, CCAI & Telecom)

Location:

Dallas, TX - Must work Onsite at Client and Tech Mahindra Office.

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., GCP).
• 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.