Principal Data Scientist - AI & Machine Learning (Databricks)

We Are Hiring!

Integrant is looking for game changers to join our team as "Data Scientist - AI & Machine Learning" with below roles and responsibilities:

• 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

Education & Experience

• 7+ years of professional experience, including 5+ years in Data Science, Machine Learning & MLOps
• MSc in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• Experience mentoring, coaching, or leading technical team members.

Data Science & Machine Learning

• 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.

MLOps & Production AI

• 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.

Generative AI & Agentic AI

• 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.

Cloud & Data Engineering

• Experience building large-scale data pipelines on Azure, AWS, or GCP.
• Experience with Databricks and Apache Spark.
• Experience with distributed data processing architectures.

Nice to Have:

Advanced AI & Data Science

• Reinforcement Learning (RL)
• Optimization techniques, including single-objective and multi-objective optimization
• Stochastic Local Search methods
• Knowledge Graphs and Graph Machine Learning.

Leadership & Consulting

• 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

• 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
• 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
• Flexible work schedules
• Perks: events, sponsored lunch, game area, rooftop hangout + more!