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Integrant

Enterprise software engineering with AI agents

Principal Data Scientist - AI & Machine Learning

Full-TimeUpdated on 10/21/2026
No salary listed
Senior
Master's
Cairo, Cairo Governorate, Egypt
Hybrid
AI & Machine LearningApplied Machine Learning

About the job

Requirements
  • At least 7 years of professional experience, including at least 5 years in Data Science, Machine Learning, and MLOps.
  • A Master of Science in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
  • Experience mentoring, coaching, or leading technical team members.
  • A 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 machine-learning 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.
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.
Desired Qualifications
  • Experience with reinforcement learning (RL).
  • Experience with single-objective and multi-objective optimization techniques.
  • Experience with stochastic local search methods.
  • Experience with 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.

About the company

Integrant builds enterprise software for regulated industries like medical devices, life sciences, pharma, financial services, manufacturing, and the public sector. Their offerings include full platform development, modernization of legacy systems, or embedding AI agents into client workflows. It uses the Agentic Software Development Life Cycle (ADLC), where AI agents assist across requirements, architecture, build, test, and deployment, while engineers with deep domain knowledge govern every step to ensure quality. Unlike many firms, Integrant combines top-tier engineering talent (the top 1% in a demanding market), a long-track record in regulated environments, and responsible AI acceleration with engineer accountability. Their goal is to be the trusted technology partner clients rely on for mission-critical software that lasts. Headquartered in San Diego with engineering centers in Cairo and Amman.

Company Size

201-500

Company Stage

N/A

Total Funding

N/A

Headquarters

San Diego, California

Founded

1992

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Simplify Jobs

Simplify's Take

What believers are saying

  • Integrant is actively hiring in August 2026, including a U.S. BDR role and engineering roles.
  • Its August 2026 materials target regulated workflows, including compliance tracking and biotech system integration.
  • The May 2026 news page and August 2026 blog show sustained product-marketing momentum, not dormancy.

What critics are saying

  • Cognizant and Cognition’s January 2026 Devin partnership commoditizes Integrant’s AI coding pitch fast.
  • Integrant’s web-heavy evidence stays marketing-only; no audited customer metrics, funding size, or revenue disclosure.
  • If enterprise buyers standardize on hyperscaler and consultancy stacks in 2027, Integrant becomes a replaceable vendor.

What makes Integrant unique

  • Integrant’s August 2026 site frames AI-orchestrated delivery across regulated biotech and life sciences.
  • Its August 2026 blog positions a proprietary agentic development life cycle as the core service edge.
  • The San Diego base plus owned offshore offices create a lower-cost, tightly managed delivery model.

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Benefits

  • Health Insurance
  • Social Insurance
  • Training Programs
  • Employee Referral Bonus