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.
Requirements
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.
Qualifications
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.
Benefits
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
