Tech Lead (AI + Full Stack/ Python) - Hands-On Builder

InVitro Capital

Egypt, Cairo

12-14 Years

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Job Description

About InVitro Capital:

InVitro Capital is a U.S.-based venture studio and fund. We build and fund companies from idea to exit, focusing on technology-driven businesses that solve real-world problems. Our portfolio spans healthcare, home services, and sales technology.

Our engineering philosophy is simple: small senior teams, extreme ownership, hands-on builders, and AI-native products. We do not bolt AI onto products we design AI-native systems from day one.

We operate with a builder culture where engineers have end-to-end responsibility for launching and scaling AI-powered products across the studio.

Role Overview:

We are looking for an exceptionally strong Tech Lead (AI + Full Stack) who codes every day and leads through architecture, execution, and example. This role is designed for elite senior builders who thrive in zero-to-one environments, enjoy solving complex problems, and can ship production-grade systems quickly.

This is a full-stack tech lead role first: you will architect and ship end-to-end product systems (backend + frontend collaboration), and you must also be strong enough in AI/ML to build and productionize AI capabilities inside those systems.

You will:

Architect core systems
Build them with your own hands
Integrate AI tooling & agents
Mentor engineers through technical leadership
Drive engineering excellence across multiple ventures

This is not a people-management role it is a high-impact IC leadership role.

What You'll Do:

Build & Lead With Technical Depth (70-90% hands-on)

Architect, build, and ship backend and full-stack systems end to end.
Lead engineers through code reviews, architecture reviews, and solid technical decision-making.
Own engineering execution across multiple ventures.

Design Scalable, AI-Native Architectures

Build modular APIs, distributed systems.
Embed AI capabilities into product systems (LLMs and/or domain AI like NLP/CV) in a way that is secure, observable, reliable, and scalable.
Design and implement retrieval workflows (RAG), evaluation/guardrails, and agent/tool orchestration where they add real product value.
Work with Python, OpenAI, Anthropic, LangGraph, LangChain, LlamaIndex, vector databases, and agent toolchains.

Engineer With Excellence

Write high-performance, production-grade code in Python (primary) for backend services and AI/ML systems.
Build and productionize ML/AI components (training/inference pipelines, model serving, and integrations) with strong engineering discipline.
Build systems optimized for reliability, performance, and observability.

Full-Stack Ownership

Collaborate with frontend engineers to deliver seamless end-to-end experiences.
Design clean APIs, interfaces, and developer workflows.

DevOps + Cloud Execution

Manage CI/CD pipelines and cloud deployments on AWS.
Kubernetes is a strong plus; AWS + Docker + CI/CD and production deployment ownership are required.
Ensure systems are scalable, fault-tolerant, secure, and well-instrumented.
Own production deployments of AI/ML components (model serving, monitoring, and lifecycle workflows) alongside the core application stack.

Technical Leadership & Mentorship

Mentor engineers and uplift technical standards across the stack.
Provide architectural direction and guide complex technical initiatives.
Contribute to hiring and shaping engineering culture.

Qualifications

Required:

Professional Engineering Experience 12+ years building and shipping production-grade systems.
Python Expertise (FastAPI or similar, production) async services, Pydantic models, you write Python daily.
Backend/System Architecture Ownership you've owned system design decisions, led code reviews, mentored engineers, and shipped end-to-end systems as a senior IC or tech lead.
Core ML / Deep Learning (hands-on) built ML/DL systems using PyTorch or TensorFlow; understand training, evaluation, optimization.
Production ML Deployment / MLOps deployed ML systems to production; owned monitoring, versioning, rollback strategies, lifecycle management.
Applied AI Systems Experience shipped AI-powered features using LLMs and/or applied NLP/CV in real products (not experimentation only).
PostgreSQL schema design, query optimization, migrations.
Cloud + Delivery AWS + Docker + CI/CD; production deployments and operational ownership.
REST API Design clean interfaces serving multiple clients (web, mobile, service-to-service).
Startup Execution experience in fast-paced, high-ownership environments.

Strong Plus:

Ruby on Rails (production) built and maintained real Rails APIs with background jobs (e.g., Sidekiq), webhooks, and complex domain logic.
Kubernetes (production) EKS (or equivalent), Kubernetes-based deployments, operational ownership.
GenAI Depth experience designing RAG systems, prompt strategies, fine-tuning workflows, and evaluation/guardrails.
Retrieval & Vector Systems vector databases (Pinecone, Weaviate, or similar), embedding strategies, namespace/tenancy design, reranking.
Agent Orchestration multi-agent patterns, tool use, chain composition (CrewAI, LangGraph, or similar).
AI Tooling Ecosystem LangChain, LlamaIndex, Hugging Face, or similar; LLM observability/tracing (LangSmith or equivalent).
Full-Stack Fluency strong collaboration with frontend teams to ship end-to-end product experiences; clean API contracts.
React 19 (JavaScript) Redux Toolkit, RTK Query, Vite, shadcn/ui.
Flutter/Dart mobile app development, BLoC pattern, Clean Architecture.
Firebase/Firestore real-time sync, Cloud Functions.
Stripe payment processing, webhook-driven architecture, Connect.
MongoDB document modeling; async drivers (Motor) is a plus.
Multimodal AI vision models; real-time audio/video AI (LiveKit, OpenAI Realtime API).
Redis + Sidekiq background job processing, caching.
01 / Venture Studio Experience building products from scratch in multi-product, high-velocity environments.

What We Offer