Lead Software Engineer, Generative AI

At Varicent, we’re not just transforming the Sales Performance Management (SPM) market—we’re redefining how organizations achieve revenue success. Our cutting-edge SaaS solutions empower revenue leaders globally to design smarter go-to-market strategies, maximize seller performance, and unlock untapped potential. Varicent stands at the forefront of innovation, celebrated as a market leader in the 2025 Forrester Wave Report for SPM , 2023 Ventana Research Revenue Performance Management (RPM) Value Index , Gartner Peer Insights , 2024 Gartner SPM Market Guide , and G2. Our solutions are trusted by a diverse range of global industry leaders like T-Mobile, ServiceNow, Wawanesa Bank, Shaw Industries, Moody's, Stryker and hundreds more. Here’s why you’ll thrive at Varicent: Innovate with Purpose: Build impactful solutions for customers worldwide. Join Excellence: Work in a diverse, collaborative, and innovative team. Shape the Future: Lead in redefining revenue optimization. Grow Together: Unlock your potential in a supportive environment. Join us at Varicent—where your talent and ambition meet limitless opportunities for success! We’re building production GenAI features and looking for a staff software developer with strong engineering skills and a passion for building scalable AI systems. You’ll contribute to the design and delivery of GenAI features, collaborate closely with technical leads and senior stakeholders, and play a key role in maintaining technical quality and scalability across a feature area. Own AI-enabled components end-to-end and deliver measurable improvements in quality and reliability. You operate independently, drive design decisions within your area, and help set strong engineering standards through reviews and mentorship. 🔧 What you’ll do Own delivery for significant features or systems: clarify requirements, design the solution, execute, ship, and iterate. Build and maintain reliable services and workflows that support AI features in production. Define how quality is measured for your area (success metrics, test strategy, evaluation approach) and automate it wherever possible. Run structured experiments, interpret results, and translate learnings into product improvements. Improve operational readiness: deployment automation, monitoring/alerting, incident follow-up, and performance/cost optimizations. Mentor teammates through design feedback, code reviews, and practical guidance. 🎯GenAI expectations Solid working knowledge of how modern AI applications are built and evaluated (you may not be a specialist, but you understand the core concepts and trade-offs). Strong learning speed: can evaluate new models/tools, assess feasibility, and recommend a path forward based on evidence and ROI. Builds solutions that are safe and compliant by design (privacy, data handling, safeguards), partnering with Legal/Security as needed ✅ Required skills & knowledge 6+ years in software engineering . 3+ years building and shipping production software in product teams. Bachelor’s in computer science, Engineering, or related field. Strong software engineering fundamentals and system design. Hands-on coding in Python and/or TypeScript (or similar), APIs/services, data pipelines. Cloud experience, CI/CD, automated testing, observability; engineers own quality without dedicated QA. Experimentation mindset: define metrics, build evals, iterate quickly; willingness to learn GenAI rapidly. 🧩 Nice to Have GenAI/LLM: prompt engineering, retrieval/RAG, function/tool-calling, evaluation pipelines, fine-tuning/LoRA, embeddings/vector search. LLMOps: experiment tracking, prompt/version control, cost/latency optimization. Tools: serverless AWS, LangChain/LangGraph, OpenAI/Anthropic/Azure OpenAI; vector DBs (Pinecone/FAISS/Milvus). External contributions (open source, blogs, talks). 🔨 How we work We balance exploration with delivery: test ideas quickly, then productionize what works. “Done” means quality goals are met; then we keep impro...