Senior AI Engineer (GenAI)

Why Join Tech9?

At Tech9, we are driven by a clear vision—to empower organizations with AI-centered solutions that make them more adaptable, efficient, and future-ready.

As a company at the forefront of innovation, we help our clients build exceptional software that not only meets today’s needs but anticipates tomorrow's challenges.

Our approach blends cutting-edge AI technology, top-tier talent acquisition, and expert project management to ensure that businesses can scale effectively and deliver high-quality, world-class software on time and within budget.

Our partnerships speak volumes, with clients like Instructure, Young Living, Imagine Learning, Mars Corp., and many others trusting us to lead the way in software development.

We are rapidly growing across our offices in the US, LATAM, and India, and we're creating an environment where talented individuals can thrive, collaborate, and have fun while building transformative solutions.

If you're excited by the opportunity to work in a fast-paced, innovative environment where scaling and building the future of software is key, we’d love to hear from you.

Join us as we work together to redefine the world of software development!

Project Overview We are partnering with a pioneering client at the forefront of immersive learning and AI-powered simulations.

Their platform enables professionals to practice and master complex interpersonal skills in high-stakes environments using cutting-edge technology that blends Large Language Models (LLMs) , speech recognition , natural language understanding , computer vision , and real-time voice synthesis .

What makes this project unique is its combination of human-in-the-loop orchestration with advanced multi-modal AI systems —bringing together live interactions, avatar control, and scalable LLM infrastructure to deliver incredibly lifelike and impactful training experiences.

The solution goes far beyond simple chatbot interfaces or single-modal agents.

This is a mission-driven environment backed by research in learning science, psychology, and AI.

The goal is to scale this system into a production-grade platform that consistently delivers predictable, stable, and low-latency GenAI performance.

If you’re passionate about building enterprise-level LLM infrastructure , working across complex AI systems, and helping shape the future of human-AI interaction—this is a rare and high-impact opportunity.

Role Overview We are hiring a GenAI Engineer to help scale our LLM-powered systems from R&D prototypes into stable, enterprise-ready infrastructure.

This is a hands-on engineering role requiring deep technical fluency and a strong sense of ownership.

You'll be designing systems that not only function reliably at scale but also handle the complexity of real-time, multi-modal interaction.

This role is ideal for someone who started in software engineering and has transitioned into AI / LLM engineering, with a demonstrated ability to deploy and maintain robust LLM systems in production.

What You’ll Be Doing Architect and scale production-grade LLM systems with a focus on reliability, latency, and predictability.

• Lead the design of LLM orchestration systems , including plugin APIs, prompt engineering workflows, and agent frameworks.
• Optimize and maintain multi-modal GenAI components , including real-time voice, NLP, and visual input modules.
• Build and maintain infrastructure on AWS , including tools and services such as SageMaker, Lambda, EKS / ECS, and DynamoDB.
• Own the stability and performance of LLM services through robust observability, monitoring, and scaling techniques.
• Translate experimental models into enterprise-ready, consistently performing products.
• Contribute to architectural decisions and roadmap planning for the AI platform.
• Actively share learnings, mentor others, and document practices to build a collaborative and high-ownership engineering culture.
• What You Need to Have 5+ years of software engineering experience , with 2+ years focused on LLM / GenAI system development and deployment .
• Proven experience scaling LLMs from R&D environments into production , with strong attention to reliability and system behavior under load.
• Deep understanding of prompt operations , plugin orchestration, and agent-based frameworks for AI applications.
• Strong fluency in AWS infrastructure , including SageMaker, Lambda, EKS / ECS, and data services like DynamoDB.
• Familiarity with frameworks like LangChain , Ray , and general LLMOps tooling for orchestration, experimentation, and deployment.
• Experience working with multi-module systems , particularly involving real-time voice detection , TTS / STT , and multi-modal agents .
• Systems-thinking approach with a commitment to maintainability, performance, and observability.
• A mindset of ownership , collaboration, and knowledge-sharing.
• Nice to Have Background in conversational AI , edtech , or simulation-based platforms .
• Prior contributions to open-source AI infrastructure projects or public LLM benchmarks.
• Familiarity with real-time simulation or gaming engines integrated with AI components.
• Interview Process The process is designed to be thoughtful, efficient, and focused on both technical ability and team fit.
• 30-minute on-demand HireVue screening , where you'll respond to situational and behavioral questions to help us understand your ownership mindset, adaptability, and approach to collaboration.

10-minute virtual Q&A session with our recruiter to clarify the role and answer any questions you may have.

This is not an interview, just a conversation to ensure alignment.

Take-home assessment (90–120 minutes), designed to evaluate your creativity and technical problem-solving in the context of LLM architecture and system design.

60-minute live technical interview with one of the AI leaders on the client team..

Expect a deep dive into your hands-on experience scaling LLM systems, your architectural thinking, and your approach to building stable, performant infrastructure.

60-minute session with the client’s CTO , focused on team fit, technical vision, and your ability to contribute to the long-term goals of the platform.

Total time investment : Approximately 4–5 hours.

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