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Home/Jobs/Senior Software Engineer II - ML Platform
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Careem

Senior Software Engineer II - ML Platform

🇦🇪 Dubai, UAE🏢 On-site
Machine LearningGenerative AIGenAIAIPythonDistributed SystemsMLOps
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Careem

Careem is building the Everything App for the greater Middle East — making it easy to move around, order food and groceries, manage payments, and more. Our purpose is simple: to simplify and improve people’s lives and build an awesome organisation that inspires.
Since 2012, Careem has enabled earnings for over 2.5 million Captains, simplified the lives of more than 70 million customers, and built a platform where the region’s best talent and entrepreneurs thrive. We operate in 70+ cities across 10 countries, from Morocco to Pakistan.

We’re now entering our next chapter — one powered by AI. We’re looking for AI talent: curious problem-solvers who know how to apply AI to build tools, automate workflows, and create real impact. Whether it’s streamlining operations, enhancing customer experience, or reimagining internal systems — we want people who can make Careem work smarter and move faster.

Careem’s ML Platform & GenAI Applications team sits at the intersection of engineering, data science, and product innovation. We provide the core tooling that lets every vertical — Ride-Hailing, Food, Groceries, Pay, and more; deliver intelligent, delightful experiences powered by Machine Learning and Generative AI. As a Senior Software Engineer on this team you’ll be a hands-on technical leader: designing distributed systems, hardening new GenAI workflows, and making it effortless for product teams to move from prototype to production while meeting Careem-class standards for reliability, cost-efficiency, and responsible AI.

What you'll do

• Design and build reusable services, APIs, and workflows that make it simple for product teams to prototype, test, and launch GenAI-driven features

• Co-create flagship GenAI applications for example, RAG-powered help-centre answers, driver-partner copilots, or marketplace-level demand forecasting assistants

• Influence architecture: keep the platform reliable, secure, and cost-effective while adding new capabilities like prompt versioning, model evaluation dashboards, or real-time feedback loops

• Champion MLOps & Responsible AI, establish guardrails, monitoring, and safe-launch practices so experiments can reach production with confidence

• Mentor teammates and share learnings through design reviews, mini-workshops, and internal demos

• Collaborate widely with designers, product managers, and data scientists to convert fuzzy product ideas into well-scoped engineering work

What you’ll need

• 7+ years of hands-on experience in software development with solid foundation in principles and design practices

• Strong expertise in Python, Java, Go, or similar programming languages

• Hands-on experience with GenAI and ML platforms—fine-tuning transformers, managing embeddings, or rolling out RAG pipelines

• Working knowledge of vector databases (Pinecone, Milvus, Weaviate) and model-aware search

• Strong foundations in software design, networking, security, observability, and CI/CD

• Experience with cloud control planes (AWS, GCP, etc.) or database internals, including query optimization

• Proven ability to drive projects from inception to production and mentor other engineers

• Experience with ML platform and MLOps is a must

Requirements

  • •7+ years of hands-on experience in software development
  • •Solid foundation in software development principles

Nice to Have

  • •Experience with prompt versioning
  • •Experience with model evaluation dashboards
  • •Experience with real-time feedback loops
  • •Experience prototyping and launching GenAI features
  • •Experience with RAG-powered help-center answers
  • •Experience with driver-partner copilots
  • •Experience with marketplace-level demand forecasting assistants

Responsibilities

  • •Design and build reusable services, APIs, and workflows for GenAI features
  • •Co-create flagship GenAI applications
  • •Influence platform architecture for reliability, security, and cost-effectiveness
  • •Champion MLOps and Responsible AI practices
  • •Mentor teammates and share learnings
  • •Collaborate with designers, product managers, and data scientists

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