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

Tech Mahindra•Englewood, CO

8d•Onsite

About The Position

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.

Requirements

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.
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).

Nice To Haves

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.

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.