AI Software Engineer

About Us

UJET leads the way in AI-powered contact center innovation, delivering a future-proof, cloud platform that redefines the customer experience with cutting-edge AI, true multimodality, and a mobile-first approach. We infuse AI across every aspect of your customer journey and contact center operations, to drive automation and and efficiency. UJET's AI solutions empower agents, optimize customer journeys, and transform contact center operations for elevated experiences and actionable insights. Built on a cloud-native architecture with a unique CRM-first approach, UJET ensures unmatched security, scalability, and prioritized data insights (without storing PII). Designed for effortless use, UJET partners with businesses to deliver exceptional interactions, smarter decision-making, and accelerated growth in the AI-driven world.

Opportunity

We’re looking for an AI Software Engineer to help build the next generation of UJET's AI products: Spiral and AXO. This is a high-impact role for someone who thrives in ambiguity, enjoys moving across the stack, and wants to turn cutting-edge model capabilities into real-world product experiences.

You’ll work across backend systems, AI services, product features, APIs, and infrastructure. One day you might be building agent workflows in Python, the next shipping customer-facing features in TypeScript, improving evals and reliability, or designing systems that process large-scale conversational data securely and efficiently.

This role is ideal for an engineer who is deeply practical about AI: someone who uses AI-assisted development naturally, understands how to build with and around LLMs, and cares about reliability, performance, and user impact as much as model quality. We value curiosity highly. The best people in this role are excited to dig into messy problems, ask good questions, and iterate until the system is measurably better.

Responsibilities

• Build and own product and platform capabilities across Spiral and AXO, from early prototypes to production systems.
• Design and implement AI-powered workflows, agent capabilities, and backend services that are scalable, secure, and reliable.
• Develop high-performance APIs, async workers, and application logic in Python and TypeScript.
• Ship user-facing product features and internal tools that make advanced AI systems useful and intuitive for customers.
• Architect and improve data ingestion, parsing, and analysis pipelines that transform raw customer interaction data into structured, actionable insights.
• Partner across engineering, product, and design to translate ambiguous product ideas into robust technical systems.
• Own infrastructure and deployment patterns on AWS/GCP, with a focus on reproducibility, observability, security, and cost efficiency.
• Improve system quality through evaluation, monitoring, logging, alerting, and operational best practices.
• Help define engineering standards, review code, and mentor teammates working on distributed systems and AI applications.

Requirements

• 3-5 years of professional software engineering experience, with strong hands-on experience building production systems.
• Strong experience as a generalist engineer who can move fluidly between backend systems, frontend, AI services, APIs, and product development.
• Excellent programming skills in Python and TypeScript.
• Experience shipping AI agents, LLM-powered applications, or other production AI systems, including prompt and tool orchestration, evaluation, and cost/reliability tradeoffs.
• Strong product instincts and comfort working in ambiguous environments where the right solution is not obvious at the start.
• Experience building and operating systems on AWS/GCP in production.
• Comfort working with large, messy datasets and building pipelines that turn unstructured inputs into dependable product functionality.
• Strong SQL and data systems fundamentals.
• A bias toward ownership, speed, and pragmatic execution.
• Experience building AI-native products from 0 to 1.
• Familiarity with conversational data, support platforms, CRM/CCaaS integrations, or customer experience tooling.
• Experience with observability, evaluation frameworks, and production reliability for AI systems.