AI-Native Fullstack Engineer - Tech Lead

About the team

You’ll run a small group inside our R&D function whose mission is to validate new product bets quickly. We move from “is this worth building?” to a working prototype in weeks, not quarters. You set the research direction, decide what to build and how, and lead the team to get it in front of users and stakeholders fast. This is a builder‑leader role: roughly hands‑on coding alongside the team, with the rest spent on strategy, planning, and unblocking people.

• Own the technical vision and execution for a portfolio of PoCs, from initial research through working prototype.
• Lead a small engineering team – set direction, review work, grow people, and keep the group focused and motivated.
• Run lightweight research: scope a problem, survey the landscape, form a hypothesis, and define what a successful prototype must prove.
• Translate fuzzy ideas into concrete plans with clear milestones, then execute against them.
• Build full‑stack – backend services, frontends, data pipelines, and the glue between them.
• Work AI‑first: use AI tooling to accelerate development, and build products that embed LLMs, agents, retrieval, and related techniques where they create real value.
• Make pragmatic build‑vs‑buy and “good enough for a PoC” calls, balancing speed against the parts that need to be solid.
• Partner with product, design, and leadership to decide which bets graduate from prototype to product.

• Proven technical leadership – has led a team or owned a major workstream, set technical direction, made the call on what to build, and shipped through others. You can run lightweight research, turn a fuzzy idea into a plan with clear milestones, and keep a small group focused and motivated. Formal management title not required; the instinct and track record are.
• Strong full‑stack engineering with real AI‑building depth – production experience across backend and frontend in at least two of C#, Python, and JavaScript/Type Script (comfortable picking up the third), and a genuinely AI‑native way of working: fluent with AI dev tools and hands‑on experience building LLM‑powered products (prompting, RAG, agents, evals). This is the builder half of a builder‑leader – you’ll write the first prototype yourself.
• Genuinely self‑directed and motivated – thrives with ambiguity, defines the problem when no one hands it to them, and drives it to a result.

• Cloud experience (AWS, Azure, or GCP) and enough Dev Ops to ship – CI/CD, containers, getting a prototype deployed, observable, and demo‑able. PoC‑grade is fine; you don’t need to be an infra specialist.

• Working understanding of ETL/ELT, data warehouses, and lakehouse concepts – enough to design a sensible pipeline and reason about how data flows through a prototype.
• Hands‑on data engineering depth is a bonus, not a requirement.

• Experience taking a 0→1 product or prototype to real users or market validation.
• A standout specialty (distributed systems, ML/AI, frontend architecture, or data engineering) on top of the breadth.
• Background in a startup, R&D lab, or innovation team where speed and ownership were the norm.
• Familiarity with experiment design, rapid user testing, or other cheap ways to validate an idea.

You’re equally comfortable whiteboarding a research plan, writing the first prototype yourself, and coaching a teammate through theirs. You’d rather ship something rough and learn than polish something no one needs. You can hold the big picture and the implementation detail at the same time, and people want to follow you because you bring clarity and momentum.

• Continuous learning and career growth opportunities
• Professional training and English/Spanish language classes
• Comprehensive medical insurance
• Mental health support
• Specialized benefits program with compensation for fitness activities, hobbies, pet care, and more
• Flexible working hours
• Inclusive and supportive culture