AI Engineering Lead - Applied AI Builder

Full 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 operatewith 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 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 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.