Senior AI Software Engineer

At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.

NiCELabs () is where

NiCEexploreswhat'snext in AI for customer experience. This roleis onAI Innovation, the Labs pillar that builds andvalidatesnew AI capabilities through prototypes, so the company can make better product decisions, faster.

We'rea deliberately small, senior team of builders inside

NiCECognigy, the AI agent platform. We operate in fast iteration cycles: spot an emerging capability, build a working prototype, validate it against real metrics (quality, latency, cost, user impact), and then decide:kill it, iterate, publish it, or hand it off to a product team. Some of our work is public, like the open-sourced

Cognigy

Platform MCP server; most of it shapes the product roadmap from the inside.

We hireforproduct mindset and builder mentality. Everyone on the team takes ideas from zero to working software, shows them to real users, and argues from evidence.

You’llwork at the intersection of frontier AI capabilities and a real enterprise product. The field moves faster than any roadmap can track. Your job is to close that gap: turn new models, protocols, and agent patterns into working systems, prove what matters in realistic scenarios, and produce the insights and reference implementations that let product and engineering teams move with confidence.

You’llneed to be comfortable with ambiguity, at home where best practices don'texist yet, and quick to move on when the results point elsewhere.

• Build agentic systems and full-stack prototypes end-to-end, shipping a first version in days and learning from real use
• Track the frontier (new models, agent patterns, protocols like MCP) and turn the promising ones into working prototypes rather than slideware
• Design experiments that show whether a concept holds up, measured in quality, latency, cost, and user impact
• Work directly with internal users, product managers, and customers to validate concepts early and iterate quickly
• Turn validated prototypes into reference implementations and write-ups that product teams can build on, then hand off cleanly and move to the next bet
• Give product and platform teams concrete feedback on where AI capabilities shine and where they fall short
• Move between initiatives as priorities shift. What you learn on one bet compounds into the next

• Have 4+ years building full-stack software, including things you started from nothing. We weigh what you've shipped more than years on a CV
• Have shipped LLM-powered systems to production:features real users depended on, not demos or notebooks
• Understand AI agents below the framework level: message arrays, tool calling, context management, prompt caching. You could build an agent loop from scratch and explain why it works
• Use AI-assisted development (Claude Code, Codex, Cursor, or similar) as your default way of working, and it makes you measurably faster, not just busier
• Arefull-stackin practice: comfortable taking a prototype from API to demo UI on your own (our stack centers on Type Script/Node.js and React)
• Do your best work when the ground is still moving. New territory energises you more than a settled plan
• Think in user problems first, solutions second

You will have an advantage if you also have:

• A strong eye for UX/UI: you can make a prototype feel like a product, and you notice when one lacks that eye
• Experience with voice and real-time AI: streaming, speech-to-text/text-to-speech, voice-to-voice models
• Ope…