Machine Learning Engineer

WitnessAI

. Build scalable pipelines to collect, preprocess, and manage datasets for LLM training and evaluation .

Posted 11/6/2026full-timeRemote • EgyptJuniorMid-Level💰 $36,000 - $48,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable machine learning pipelines and evaluating model performance through robust experimentation and data visualization. Proficient in Python and familiar with cloud platforms and containerization technologies to enhance model deployment and monitoring.

Highest-signal resume keywords

Machine LearningPython ProficiencyCI/CD Tools ExperienceData Engineering ToolsML Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceSupervised LearningUnsupervised LearningDeep LearningNatural Language ProcessingExperiment DesignModel EvaluationData PreprocessingModel Optimization

Soft Skills

CollaborationAdaptabilityProblem-Solving

Tools & Technologies

PyTorchSparkKafkaAirflowAWSGCPAzureDockerKubernetesTesting Frameworks

Industry Keywords

Adversarial Machine LearningModel RobustnessExplainable AICybersecurityAnomaly Detection

Tech Stack

Tools & technologies

AirflowAWSAzureCyber SecurityDockerGoogle Cloud PlatformKafkaKubernetesPythonPyTorchSpark

About the role

Key responsibilities & impact

Build scalable pipelines to collect, preprocess, and manage datasets for LLM training and evaluation
Design and run experiments evaluating LLM accuracy, robustness, fairness, and safety
Create dashboards, reports, and visualizations communicating evaluation results, trends, and failure cases
Develop and leverage knowledge graphs to structure data, enrich evaluation, and improve context-driven model performance
Collaborate with researchers to translate new ideas into engineering workflows
Collaborate with data scientists to automate QA checks and guardrails
Fine-tune, optimize, and integrate models into production systems
Apply reliability, scalability, monitoring, and CI/CD best practices
Contribute to ML tooling and experimentation frameworks

Requirements

What you’ll need

2–5+ years working in machine learning or data science
Strong software engineering background
Proficiency in Python
Experience with testing frameworks such as pytest or unittest
Experience with CI/CD tools
Proficiency in ML frameworks such as PyTorch
Experience with data engineering tools such as Spark, Kafka, or Airflow
Familiarity with AWS, GCP, or Azure
Familiarity with Docker and Kubernetes
Strong knowledge of supervised and unsupervised learning, deep learning, and NLP
Interest or background in cybersecurity, adversarial ML, anomaly detection, or related fields
Comfortable working in fast-moving, ambiguous startup environments
Research or industry experience in adversarial ML, model robustness, or explainable AI is nice to have
Experience building interactive dashboards for model monitoring and visualization is nice to have
Contributions to open-source ML, NLP, or security projects are nice to have