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 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 a highly skilled AI Engineering Lead who thrives at the frontier of applied AI — someone who builds real-world ML and LLM systems, not academic experiments. This role is designed for senior builders who enjoy crafting end-to-end AI pipelines, optimizing models for production, and integrating AI capabilities directly into high-scale products.
You will:
Design and train advanced models
Build and optimize data and inference pipelines
Deploy AI systems to production with reliability and scale
Collaborate closely with backend and product teams
Drive excellence across the AI lifecycle
This is a hands-on senior IC role for engineers who want to build AI systems that matter.
What You'll Do
Build End-to-End AI Systems
Architect and implement data pipelines for training, evaluation, and real-time or streaming inference.
Build, fine-tune, and integrate ML, NLP, LLM, and/or Computer Vision models using Python, PyTorch, TensorFlow, and Hugging Face.
Implement retrieval pipelines, embeddings, and vector database integrations.
Deploy Production-Grade AI
Ship reliable, high-performance inference services using Docker, Kubernetes, and cloud platforms (Azure preferred).
Design APIs and microservices that integrate models into user-facing applications.
Optimize inference latency, throughput, and cost efficiency.
Model Monitoring & Improvement
Track model drift, accuracy, performance, and stability.
Continuously improve production models through retraining, evaluation, and enhancements.
Implement observability and monitoring across the ML lifecycle.
Champion MLOps Excellence
Maintain CI/CD pipelines for ML systems.
Set up and manage experiment tracking, model registries, and reproducibility workflows.
Ensure robust automation and smooth model deployment processes.
Required
10+ years of experience building and deploying ML/AI systems in production.
Advanced proficiency in Python, with strong expertise in PyTorch or TensorFlow.
Strong understanding of machine learning, deep learning, data engineering, and distributed training.
Hands-on experience with LLMs, NLP, CV, or recommender systems.
Strong MLOps and cloud-native engineering experience.
Experience deploying AI systems on Azure, AWS, or GCP.
Proficiency with Docker, Kubernetes, and scalable microservice architectures.
Strong debugging, optimization, and performance tuning abilities.
Experience working in fast-paced, high-ownership startup environments.
Excellent communication and cross-functional collaboration skills.
Huge Plus
Experience with streaming inference or real-time ML systems.
Familiarity with monitoring tooling such as Prometheus, Grafana, or ELK/EFK.
Contributions to open-source ML/AI projects or a strong GitHub portfolio.
Experience building 0→1 systems or working in high-growth technical environments.
What We Offer
Compensation: $3,000-$3,800 USD/month base + bonus
Health insurance
Social insurance
Paid Time Off (PTO)
High ownership and autonomy
Opportunity to build advanced AI systems across multiple ventures
A culture optimized for speed, impact, and technical excellence
Schedule & Work Setup
Cairo-based candidates preferred
Hybrid: expected at the Cairo office at least once per week
Monday-Friday, aligned with U.S. Pacific Time
High-autonomy, high-velocity engineering environment
