AI Engineering Lead

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.

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.

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