Senior Machine Learning Engineer (Dubai, Dubai, AE, 118353)

SAP
Dubai
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 7-9 yearsSkills: ["Ownership","Strategic thinking","Product judgment","Mentoring","Customer-centric","Clarity","Continuous learning"]

Lead the design and delivery of production-grade machine learning and generative AI systems to solve complex product and business problems at scale. Own end-to-end architecture across data pipelines, feature stores, training workflows, model serving, online experimentation, and observability. Drive advanced deep learning, NLP, ranking/recommendation, forecasting, semantic retrieval, and LLM/RAG applications, improving reliability, latency, and cost. Mentor engineers and advance MLOps and responsible AI practices.

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FursaFursa
SAP
SAP
2 hours ago

Senior Machine Learning Engineer (Dubai, Dubai, AE, 118353)

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 2 hours agoStatus: Live

Job Summary

Lead the design and delivery of production-grade machine learning and generative AI systems to solve complex product and business problems at scale. Own end-to-end architecture across data pipelines, feature stores, training workflows, model serving, online experimentation, and observability. Drive advanced deep learning, NLP, ranking/recommendation, forecasting, semantic retrieval, and LLM/RAG applications, improving reliability, latency, and cost. Mentor engineers and advance MLOps and responsible AI practices.
Location: Dubai
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Lead architecture and implementation for production-grade ML and generative AI systems across data pipelines, feature stores, training workflows, and model serving.
  • •Drive advanced use cases spanning deep learning, NLP, ranking/recommendation, forecasting, semantic retrieval, and LLM/RAG applications.
  • •Improve live performance and reliability by optimizing for latency, cost efficiency, and observability in production environments.
  • •Guide technical direction on model selection, distributed training/inference, GPU utilization, compression, prompt/retrieval optimization, drift detection, and retraining strategy.
  • •Mentor engineers and turn best practices into reusable patterns and platform capabilities, including responsible AI controls and governance.
Travel: Low travel

Key Requirements

  • •Expert-level Python programming with strong software engineering experience using Java or Go to build production-grade systems.
  • •Deep knowledge of machine learning, deep learning, and optimization for structured data, NLP, search, ranking, and recommendation.
  • •Experience designing and operating end-to-end ML systems across ingestion, experimentation, deployment, observability, and lifecycle management.
  • •Hands-on experience with modern ML/LLM tooling such as PyTorch, TensorFlow, and scikit-learn, including fine-tuning, evaluation, orchestration, and model serving.
  • •Experience building generative AI applications using embeddings, vector databases, RAG pipelines, agent workflows, prompt engineering, and guardrails.
Experience:7-9 yearsMachine learningGenerative AIMLOpsCloud-nativeNLPRecommendation systemsLLM applications
Skills:OwnershipStrategic thinkingProduct judgmentMentoringCustomer-centricClarityContinuous learning
Tech Stack:PythonJavaGoPyTorchTensorFlowScikit-learnDeep learningNLPLLMsEmbeddingsVector databasesRAGPrompt engineeringGuardrailsMLOpsFeature storesCI/CDInfrastructure as codeExperiment trackingModel registries

Company Brief

SAP
Global enterprise software company best known for ERP systems and business applications covering finance, supply chain, procurement, HR, analytics, and customer management for large organizations.
Industry: Enterprise Software
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Walldorf, Germany
Founded: 1972
Glassdoor
Glassdoor: 4.1
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