N-iX is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join N-iX and become part of a team where your ideas make a real impact. We are committed to continuous innovation and are now strategically leveraging Generative AI to significantly enhance our Software Development Life Cycle (SDLC) productivity.We are looking for a skilled Gen AI Adoption Lead (Engineering Productivity) to join our team.We’re building a small, sharp team to reinvent how software gets built—using GenAI to augment the SDLC end-to-end. You’ll lead by doing: pilot use cases, wire tools into real workflows (CLI/IDE/CI), and teach teams to ship faster with higher quality. No hype—measurable impact.You will be joining a core, 6-person GenAI Adoption group inside Engineering. We partner directly with delivery teams to make AI a daily habit (coding, tests, reviews, docs, DevOps). Think: Copilot/Claude/Cursor + scripts/agents + CI hooks. We’re remote-first across the EU/EE (PL/UA welcome).Key Responsibilities: Embed AI in the SDLC: turn “nice demo” into daily CLI/IDE workflows and CI steps. Run fast pilots: pick 2–3 high-ROI use cases per account, ship in weeks, measure, scale. Build glue + agents: small tools, prompts, and automations that actually stick. Coach by pairing: live code-along, short playbooks, internal demos. Measure outcomes: baseline → deltas (cycle time, PR throughput, defects, time saved). Keep it safe: model/provider choices, data handling, guardrails, policy basics. Requirements: 5+ years as hands-on engineer (Java or .NET great; Python/TS welcome). Daily driver of GenAI coding tools (Copilot, Claude Code, Cursor, Code LLMs). Comfort with CLI/IDEs/CI (GitHub/GitLab, JetBrains, VS Code, Bash), and wiring APIs. You’ve shipped at least one AI-assisted workflow (tests, codegen, review bot, docs). You can teach: workshops, pairing, short guides. Clear English, straight talk. Pragmatic about quality & security (you know when AI output is not production-ready). Bonus: AWS/Azure, Kubernetes/Terraform, prompt patterns, lightweight agent orchestration. You may not like the role if: You prefer slideware over shipping. You avoid terminals and CI—this is engineer-first, not tool theater. You want certainty. We iterate, measure, and change course fast.