Lead a small engineering team at Usersnap (saas.group), combining hands-on AI feature development with team leadership, technical direction and delivery ownership.
Own team coordination, delivery timelines, workload allocation, and project follow-through for a small engineering team.
Keep work moving: break down ambiguous goals, sequence them with Product, flag slippage early, and make sure commitments are met.
Give clear, constructive technical feedback in code review, design discussions, and 1:1s, and raise standards without demotivating people.
Grow the team's capability, including how we use AI tooling to work faster without lowering quality.
Assess the current engineering bar and raise it. Define what "good" looks like for AI-native development.
Set expectations in observability, testing, security/compliance, scalable architecture, and timeboxing.
Own or steward key architecture decisions, including model selection and integration approach.
Flag technical risks early, especially around AI reliability, cost, and edge-case behavior.
Design, build, and ship AI-powered features alongside the team, from prototyping through production quality.
Work hands-on with LLMs, embeddings, and related AI tooling to solve real product problems.
Build across the stack as needed.
Partner with Product on requirements and priorities, pushing back with technical reality when needed.
Partner with company leadership on technical planning, resourcing, and sequencing.
Requirements
Hands-on technical leadership with real engineering vision. You've led delivery for a small team, ideally for 2-3 years of people leadership.
A style that raises the bar. You give direct, specific technical feedback and hold high standards.
Delivery discipline. You're comfortable owning timelines, allocating work, and following projects through to the end.
7+ years of software engineering experience, and you've shipped AI features to production and owned the lifecycle from problem framing to post-launch iteration.
Strong fullstack fundamentals. Comfortable across frontend, backend, and infrastructure as needed.
Practical experience with LLM APIs, prompt engineering, RAG, or similar techniques in live systems.
Observability and testing rigor. You build in logging, monitoring, and tracing from the start.
Scalable architecture and data pipelines. Solid SQL, schema design, and the ability to design pipelines.
Evaluation mindset. You define success metrics, design experiments, run offline and online evaluations.
Production readiness. Docker fluency, CI/CD, and good habits for versioning prompts, models, and datasets.
LLM platform breadth. Experience integrating multiple LLM providers, working with vector databases, and using LangChain.
Security and compliance experience, or a strong interest in it.
Practical timeboxing of AI development work.
You identify problems and propose solutions on your own, rather than waiting to be told exactly what to build.
You're comfortable in a lean team where you own outcomes, not just tickets.
Done for you
We'll apply to this for you
Denidel's HR team submits and tracks the application on your behalf.
