Software Engineer – Machine Learning

Advansys

. Lead the entire ML lifecycle from data collection and analysis to model deployment, monitoring, and optimization.

Posted 10/6/2026full-timeNasr City • EgyptMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the machine learning lifecycle, from data collection and analysis to model deployment and optimization, with a strong focus on deep learning, NLP, and MLOps practices. Proficient in Python and relevant ML frameworks, capable of collaborating with cross-functional teams to deliver robust ML solutions.

Highest-signal resume keywords

Machine Learning EngineeringDeep LearningNatural Language ProcessingMLOpsPython Programming

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningNatural Language ProcessingData AnalyticsStatistical AnalysisFeature EngineeringProduction Code QualityML FrameworksCI/CDContainerization

Soft Skills

Problem-SolvingCommunicationCollaboration

Tools & Technologies

TensorFlowPyTorchScikit-learnDockerKubernetesAWSGCPAzure

Industry Keywords

MLOps Best PracticesGenerative AIConversational AIData StructuresAlgorithmsSystem Design

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesPythonPyTorchScalaScikit-LearnTensorflow

About the role

Key responsibilities & impact

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, 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

What you’ll need

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.