Senior MLOps Engineer at Intelmatix Limited

, , Egypt -

Full Time

Start Date

Immediate

Expiry Date

04 Jul, 26

Salary

0.0

Posted On

05 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, MLOps, DevOps, AWS, Docker, Kubernetes, CI/CD, LLM, Infrastructure as Code, Prometheus, Grafana, MLflow, FastAPI, Data Engineering, Observability, Cloud Architecture

Industry

Software Development

Description

Job Summary / Position Overview: We are looking for a skilled Senior MLOps Engineer based in Egypt to join our team. 🧑 💻This role sits at the crossroads of engineering, data science, and product development, supporting both our in-house ML products and research/client projects. You will work closely with the DevOps team to design scalable cloud infrastructure and with the DS team to automate, optimize, and deploy machine learning pipelines. The ideal candidate has strong Python and cloud expertise, thrives in a collaborative environment, and enjoys turning cutting-edge research into robust, production-grade systems that power real-world applications. Key Responsibilities: ● Own the full ML lifecycle—develop, deploy, monitor, and continuously improve models and infrastructure. ● Build and optimize CI/CD pipelines for both software and ML workflows, ensuring smooth end-to-end automation. ● Design, deploy, and manage cloud infrastructure (preferrably AWS) to support training, serving, and scaling ML workloads. ● Automate deployment and orchestration, managing environments with modern DevOps/MLOps tools and best practices. ● Develop, orchestrate, and deploy LLM workflows, ensuring they are production-ready, scalable, and reliable. ● Ensure reliability and scalability of production systems while maintaining monitoring and observability, performance, and security. ● Collaborate across teams, working closely with software engineers, data scientists, and product managers to deliver ML-powered features. ● Proactively troubleshoot and resolve issues, from infrastructure bottlenecks to model performance concerns. ● Document and standardize workflows, infrastructure, and operational procedures for reproducibility and clarity. ● Drive innovation and adopt best practices in MLOps, DevOps, and infrastructure engineering to continuously enhance systems. Minimum Qualifications: ● Experience: 5+ years in MLOps, DevOps, or infrastructure-focused engineering roles, delivering and operating ML projects in production. ● Programming & ML: Strong proficiency in Python, deep understanding of cutting edge ML concepts and understanding of ML/LLM workflows and constraints. ● Cloud & Infrastructure: Hands-on experience with AWS, including ECS, and designing scalable, secure, and observable cloud architectures for ML workloads. ● Containerization & Orchestration: Expertise with Docker, Kubernetes, and managing multi-stage ML workflows. ● CI/CD & DevOps Tools: Experience with GitHub Actions, infrastructure as code, automated testing, and monitoring tools like Prometheus, Grafana, Loki, and Opentelemetry. ● ML Platform Tools: Familiarity with ML platforms, workflow orchestration, experiment tracking, and building production-grade APIs for serving models (e.g., MLflow, Prefect, FastAPI, or equivalents). ● LLM Development & Deployment: Hands-on experience with LLM workflows, including model orchestration, deployment, and serving using tools like LangGraph, Flowise, or equivalents. ● Data & Monitoring: Knowledge of ETL pipelines, large-scale data processing, observability, telemetry, and alerting best practices. ● Collaboration & Communication: Ability to work with research engineers, software teams, and product managers, translating technical constraints into production-ready solutions. ● Mindset: Balances reliability with velocity, ensuring reproducible, secure, and high-performing ML systems while enabling teams to ship fast. Location Egypt (Remote) Department Machine Learning & Data Science Employment Type Full-Time Minimum Experience Experienced

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Responsibilities

The Senior MLOps Engineer will own the full machine learning lifecycle, including developing, deploying, and monitoring models and infrastructure. They will also collaborate with cross-functional teams to build scalable CI/CD pipelines and manage cloud infrastructure for ML workloads.