Tech Lead

Tech Lead

Location: Remote (Brasília UTC-3 or Dubai GMT+3 preferred) | Team: Product Engineering

Seniority: 3-6 yrs production experience | Reports to: CEO

Stack: Python / TypeScript / React + LLMs / Cloud-Native

About Mundos

We're Mundos, the world's first AI-native Venture Builder architecting the next generation of intelligent, high-impact businesses. Unlike traditional incubators or studios, we embed advanced AI capabilities from day zero, transforming how ventures are conceived, built, and scaled globally.

We operate at the convergence of visionary strategy and technical execution; identifying opportunities not visible to the naked eye, then rapidly materializing them through our proprietary AI venture building methodology and fast-paced engineering muscle. Working alongside forward-thinking partners across MENA and LATAM, we're not just implementing AI; we're fundamentally rethinking business models around AI's capabilities.

While others talk about AI transformation, we're already shipping it: moving with startup velocity but maintaining institutional-grade discipline and quality and seamless user experiences. Our globally distributed team unites serial entrepreneurs, AI researchers, and seasoned operators who share one trait: the ability to translate cutting-edge AI capabilities into tangible business impact.

We're seeking an experienced senior software engineer who thrives in high-velocity environments, designs complex scalable architectures, ships production-ready code across the full stack, and is eager to lead our team of developers.

The Mission

Lead a squad of 2-4 Software Engineers to:

1. Architect clean, evolvable systems—from high-level platform topology down to LLD and code patterns.
2. Ship production-grade services spanning backend APIs, vector-search RAG pipelines, LLM inference systems, and polished React experiences.
3. Mentor & Multiply—raise engineering standards, run design reviews, pair, coach, and unblock teammates.
4. Own Reliability—non-negotiable SLIs/SLOs, observability, on-call rotation design, and post-mortems for AI services at scale.
5. Drive Roadmaps—partner with Product & Venture leads to translate fuzzy business bets into executable backlogs.
6. Elevate DevEx—CI/CD, IaC, test automation, and docs that keep velocity high without chaos.

What You'll Do (Day-to-Day)

- System Design — Draft high-level and low-level designs for multi-tenant web services that hit tight latency targets; architect scalable AI service deployments; weigh build-vs-buy decisions for data stores and ML infrastructure; model event-driven workflows with message queues (Kafka, RabbitMQ, etc.).
- End-to-End Development — Write and review production code across backend (Python/TypeScript/Node/Go) and frontend (React); establish coding standards for AI service integration, enforce domain boundaries, and drive automated test coverage.
- AI/ML Infrastructure — Design and deploy LLM inference pipelines, implement model serving architectures, establish benchmarking frameworks for model performance evaluation, and optimize AI service latency and throughput at scale.
- Data & Caching — Design relational schemas, optimize queries, implement Redis (or similar) caching layers for AI workloads, and plan sharding/partitioning strategies as traffic grows beyond 100k DAU.
- Infrastructure & DevOps — Author Docker/Kubernetes artifacts and Terraform/CDK stacks; build CI/CD pipelines for ML model deployments; automate blue-green or canary deployments on AWS/GCP; define runbooks and alerting for AI services.
- Performance & Reliability — Instrument AI services, track SLIs/SLOs for model inference, run load tests on LLM endpoints, lead post-mortems, and continually improve latency, throughput, and availability of AI systems.
- Technical Leadership — Conduct weekly 1-on-1s, run design and code reviews, mentor engineers on AI system architecture, and align the squad with OKRs while fostering psychological safety and async excellence.

Core Requirements

- 5+ yrs building & scaling production systems (startups or Tier-1 tech).
- AI Systems Experience: Proven track record architecting and deploying AI services at scale, including LLM inference pipelines, model serving architectures, and ML infrastructure.
- LLM Expertise: Hands-on experience with LLM benchmarking, performance evaluation, and optimization of large language model deployments in production environments.
- Demonstrated system-level thinking—can white-board trade-offs across API, DB, cache, AI infrastructure, and cost.
- Polyglot backend strength—Python (FastAPI, Django, or Flask) and TypeScript/Node; React mastery on the front.
- Cloud-native & Kubernetes: Container orchestration with strong Kubernetes experience, CI/CD, IaC (Terraform/CDK/Pulumi), observability stacks (Prometheus/Grafana, OpenTelemetry).
- Ownership mindset: you design it, you ship it, you wake up if it breaks, you iterate until it sings.

Bonus Points

- Prior venture-builder or 0 → 1 startup experience—wearing many hats.
- Real-world LangGraph agent-orchestration or autonomous workflow pipelines.
- MLOps expertise: Model versioning, A/B testing frameworks for AI models, automated retraining pipelines.
- Experience with Kafka streams, event-sourcing, or CQRS in production.
- Security by design (OWASP, IaC policy enforcement, secret management).
- Published tech articles, OSS maintainer, or conference talks.

Why Join Mundos

- Venture Building DNA: Your code doesn't just ship features; it builds entire businesses that can scale independently
- Small remote team, huge canvas: your code lands in production within days, not quarters.
- Global Impact: Work on ventures that span multiple markets, cultures, and business models
- Exponential Learning: Exposure to multiple ventures means accelerated growth across domains and technologies
- Competitive Compensation: USD salary, equity (ESOPs) in our venture ecosystem, flexible remote work, and a clearly defined growth trajectory

How to Apply

Send the following information to talent@mundos.xyz:

1. Résumé, GitHub (or equivalent), and a short cover letter
2.

3. In three sentences, describe a system you architected and shipped to production that scaled an order of magnitude without downtime.
4.

5. Include one link (PR, blog post, design doc excerpt, or talk) that showcases your system-design thinking. (People from an OSS background like repository maintainers will be our first preference)

Incomplete submissions won't be reviewed—attention to detail is part of the job.

Join Mundos to engineer the AI-native ventures that tomorrow's economy will run on. Let's build what's next—today.

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