Hybrid Tech Lead – AI, Full Stack

at Jesica.ai

• Job title: Tech Lead – AI, Full Stack
• Job type: Full Time
• Experience level: Senior
• Salary: $4,000 - $5,000 per month
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Cloud, Dart, Distributed Systems, Docker, Firebase, Flutter, JavaScript, Kubernetes, MongoDB, Postgres, Python, PyTorch, React, Redis, Redux, Ruby, Ruby on Rails, Tensorflow
• Location requirements: Hybrid, Cairo, Egypt

About the role

• Tech Lead (AI + Full Stack) at InVitro Capital building AI-native products with hands-on engineering and leadership.

Responsibilities

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

Requirements

• 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.
• 0→1 / Venture Studio Experience — building products from scratch in multi-product, high-velocity environments.

Benefits

• Health insurance
• Social insurance
• Paid Time Off (PTO)
• High ownership and autonomy
• Opportunity to build multiple AI-powered products from scratch
• A culture optimized for speed, impact, and technical excellence