Varicent - Staff Gen AI Developer
Requirements
• 6+ years in software engineering. • 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. • Strong software engineering fundamentals • Hands-on coding in Python and/or TypeScript (or similar), APIs/services, data pipelines. • Python and/or TypeScript • 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. • 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 improving accuracy, cost, and performance. • The team owns systems end-to-end (build, release, reliability). • For this role, the estimated annual base salary range is between $990,600.00 - $1,300,000.00 (MXN). In addition to base salary, our compensation package may include bonuses, commissions for eligible sales roles, and a comprehensive benefits package. The actual base salary will vary based on factors including individual qualifications and market data, as objectively assessed during the interview process. • This posting is for a new vacancy. • This hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement — not replace — human decision-making.
Responsibilities
• 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
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