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Integrant

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Principal Data Scientist - AI & Machine Learning (Databricks)

Summary

Principal Data Scientist designs, builds, and deploys AI/ML models and RAG systems, leads MLOps pipelines, and mentors teams to solve complex client challenges using Python, LLMs, and cloud platforms.

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!
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What this application asks

workable

First name, Last name, Email, Headline, Phone, Address, Photo, Education, Experience, Summary, Resume, Cover letter, What is your nationality?, Are you comfortable working in hybrid model?, Are you comfortable working with US timing? (12:00 PM to 8:00 PM), What is your Military status?, Have you ever been employed by Integrant before? If yes, when?, Do you have any friends/relatives working at Integrant? If yes, please specify their names., What is your notice period?, Do you have limitations working on certain industries? If yes, please specify., Are you willing to provide Clearance/Resignation letter (Form 6), Linkedin Profile Link:

  • How many years of experience do you have all in all in tech industry?
  • How many years of experience do you have as a Data Scientist?
  • Do you have a Master's degree? If so, what is its current status? choose one
  • If yes, in which field is your Master's degree?
  • How many peer reviewed publications do you have?
  • Do you have any other degrees, diplomas, or certifications you'd like to mention? written answer
  • What is the biggest data set that you processed, and how did you process it, what were the results? written answer
  • What is the latest research you did to develop new algorithms and methods to address specific client needs? written answer
  • Do you have client engagement experience? yes / no
  • If yes, please specify the region and industry.
  • Which industries/domains have you worked in?
  • How many years of experience do you have in classification, clustering, regression and association rule mining? Please mention frameworks you have used?
  • Have you been involved in building Generative AI applications? yes / no
  • If yes, please specify the type of applications (e.g., Chatbots, Image Generation Applications, etc.).
  • Do you have experience with ML cloud services? yes / no
  • If yes, please specify the services you have used
  • Do you have hands-on MLOps experience? choose one
  • Which MLOps platforms, cloud providers, and tools have you used professionally?
  • Do you have hands-on experience with RAG and Vector Databases? yes / no
  • Do you have hands-on experience with LangChain, LangGraph, or similar? yes / no
  • If Yes, which framework(s) have you used professionally?
  • Do you have hands-on experience with LlamaIndex, AI Agents, or multi-agent frameworks? yes / no
  • Which framework(s) have you used professionally?
  • Do you have hands-on experience with MCP? yes / no
  • Do you have hands-on experience with Prompt Engineering and LLM Evaluation tools? yes / no
  • Which tools have you used professionally?
  • Do you have hands-on experience customizing LLMs through Fine-Tuning, LoRA, or Embedding techniques? yes / no
  • Can you describe your hands-on experience designing and building large-scale data pipelines on Azure, AWS, or GCP using Databricks and Apache Spark?

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