# Senior Machine Learning Engineer

**Company:** [Advansys](http://jobs.workable.com/companies/gJuBiNFMLE7eoxCqoDL9m7.md)
**Location:** Nasr City, Egypt
**Workplace:** hybrid
**Employment type:** Full-time
**Department:** ADVANSYS

[Apply for this job](http://jobs.workable.com/view/54438044-3f95-4436-9990-76b9947761e2)

## Description

Key Responsibilities

-   Lead the entire ML lifecycle from data collection and analysis to model deployment, monitoring, and optimization.
-   Apply deep learning and NLP techniques to develop solutions, potentially enhancing systems like search or recommendation engines.
-   Design and implement end-to-end ML pipelines, incorporating MLOps best practices for CI/CD, containerization (Docker, Kubernetes), and cloud deployment ([AWS](https://www.google.com/url?sa=i&source=web&rct=j&url=https://aws.amazon.com/&ved=2ahUKEwjut5TqtKORAxVORKQEHe06F34Qy_kOegYIAQgDEAM&opi=89978449&cd&psig=AOvVaw3DQcDXGUZt4EDWDMJX_xsH&ust=1764919823141000), GCP, Azure).
-   Utilize LLM knowledge, including prompt engineering and fine-tuning, to build advanced generative AI applications and conversational AI solutions.
-   Perform comprehensive data analytics, including statistical analysis and feature engineering, to inform model development and extract actionable insights from large datasets.
-   Write production-quality, robust code in Python (and potentially other languages like Java or Scala), ensuring code quality through reviews and testing.
-   Collaborate with cross-functional teams, including data scientists, data engineers, and product managers, to translate business requirements into technical ML solutions.

## Requirements

Required Skills and Qualifications

-   Proven experience as a Machine Learning Engineer with a strong portfolio of deployed production models.
-   Proficiency in Python and relevant ML frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn).
-   Expertise in data science methodologies, statistical analysis, and data analytics.
-   Hands-on experience with MLOps tools and practices for managing the ML application lifecycle.
-   Strong understanding of NLP and experience with LLMs and prompt engineering techniques.
-   Solid software engineering background with knowledge of data structures, algorithms, and system design.
-   Excellent problem-solving, communication, and collaboration skills.