Principal Data Scientist – AI & Machine Learning

Integrant, Inc.

Data Scientist specializing in AI & Machine Learning with Integrant. Using AI techniques to solve complex client challenges while mentoring junior team members.

Posted 6/10/2026full-timeCairo • 🇪🇬 EgyptLeadWebsite

Tech Stack

Tools & technologies

ApacheAWSAzureCloudGoogle Cloud PlatformKerasPythonPyTorchScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.
Design, develop, evaluate, and deploy predictive and prescriptive machine learning models.
Conduct open research and experimentation to develop innovative solutions for complex client challenges.
Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.
Design and implement end-to-end Machine Learning and Generative AI solutions.
Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications.
Develop scalable model deployment and monitoring solutions using MLOps best practices.
Monitor model performance, detect concept drift, and continuously improve deployed systems.
Collaborate with software engineering teams to productionize AI applications and ensure reliability, scalability, and maintainability.
Mentor and coach junior Data Scientists and Machine Learning Engineers.
Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.
Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks.

Requirements

What you’ll need

7+ years of professional experience, including 5+ years in Data Science and Machine Learning.
MSc in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
Experience mentoring, coaching, or leading technical team members.
Strong foundation in Machine Learning techniques including Classification, Regression, Clustering, Association Rule Mining, Feature Engineering, and Model Evaluation.
Experience with Deep Learning concepts and frameworks.
Extensive hands-on experience with Python and the Data Science ecosystem.
Experience with one or more ML frameworks such as Scikit-Learn, TensorFlow, Keras, or PyTorch.
Experience conducting research, experimentation, and hypothesis-driven analysis.
Experience deploying and managing Machine Learning models in production environments.
Experience monitoring model performance, detecting concept drift, and driving continuous improvements.
Hands-on experience with MLOps practices, CI/CD pipelines, model versioning, experiment tracking, monitoring, and observability.
Experience deploying AI/ML solutions on cloud platforms such as Azure, AWS, GCP, or Databricks.
Experience with ML platforms and services including Azure ML, AWS SageMaker, or Google Vertex AI.
Familiarity with deployment and serving tools such as MLflow, FastAPI, and Streamlit.
Hands-on experience building Retrieval-Augmented Generation (RAG) solutions and semantic search applications.
Experience working with Vector Databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.
Experience using LLM orchestration frameworks such as LangChain, LangGraph, or similar technologies.
Experience working with Agentic AI frameworks such as LlamaIndex, CrewAI, AutoGen, or equivalent.
Experience implementing MCP (Model Context Protocol), tool calling, and function-calling workflows.
Strong understanding of prompt engineering techniques and LLM optimization.
Experience evaluating LLM applications using frameworks such as LangSmith, RAGAS, or similar tools.
Experience with Embeddings, Vector Retrieval, Semantic Search, Fine-Tuning, and LoRA techniques.
Nice to Have:
Reinforcement Learning (RL)
Optimization techniques, including single-objective and multi-objective optimization
Stochastic Local Search methods
Knowledge Graphs and Graph Machine Learning.
Experience building large-scale data pipelines on Azure, AWS, or GCP.
Experience with Databricks and Apache Spark.
Experience with distributed data processing architectures.
Experience leading AI initiatives and technical strategy.
Experience working directly with international clients and stakeholders.
Experience defining AI architecture, standards, and best practices across teams.

Benefits

Comp & perks

Salary paid in USD
Six-month career advancing opportunities
Employee parking space
Supportive and friendly work environment
Premium medical insurance [employee +family]
English language development courses
Interest-free loans paid over 2.5 years
Technical development courses
Planned overtime program (POP)
Employment referral program
Premium location in Maadi & Nasr City
Social insurance
Opportunity to travel and work onsite with U.S. customers
In-house Technical and English training programs
Dedicated learning time (check out our 4Plus1 Program)
Flexible work schedules
Perks: events, sponsored lunch, game area, rooftop hangout + more!

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningartificial intelligencedata sciencedeep learningclassificationregressionclusteringfeature engineeringmodel evaluationMLOps

Soft Skills

mentoringcoachingleadershipclient engagementcollaborationcommunicationproblem-solvinginnovationtechnical discussionsknowledge transfer

Certifications

MSc in Computer ScienceMSc in Data ScienceMSc in Artificial IntelligenceMSc in StatisticsMSc in MathematicsMSc in Engineering