Machine Learning Engineer

AI71
Abu Dhabi
Full timeFunction: Data Science & Machine LearningExperience: 5+ yearsSkills: ["Communication","Collaboration","Algorithmic problem-solving"]

Lead end-to-end development and deployment of advanced AI models across generative AI and traditional machine learning. Build LLM/NLP pipelines for regulation parsing and semantic search (RAG) for LeverEDGE, and forecasting, risk scoring, and optimization for Intelligent Supply Chain. Own MLOps and productionization—containerizing models, orchestrating training/inference, and monitoring drift—while delivering robust, explainable solutions in defense-grade secure environments.

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FursaFursa
AI71
AI71
3 months ago

Machine Learning Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 4 hours agoStatus: Live
Reposted: similar role first listed 1 year ago

Job Summary

Lead end-to-end development and deployment of advanced AI models across generative AI and traditional machine learning. Build LLM/NLP pipelines for regulation parsing and semantic search (RAG) for LeverEDGE, and forecasting, risk scoring, and optimization for Intelligent Supply Chain. Own MLOps and productionization—containerizing models, orchestrating training/inference, and monitoring drift—while delivering robust, explainable solutions in defense-grade secure environments.
Location: Abu Dhabi
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design and fine-tune LLM pipelines for regulation parsing, including interpreting complex regulatory texts and extracting structured rules.
  • •Implement RAG-based semantic search over technical documentation and historical project data, and optimize prompt strategies for domain tasks.
  • •Develop forecasting engines, risk scoring, anomaly detection models, and multi-objective optimization algorithms to support procurement decisions.
  • •Containerize models and deploy them into secure, on-premise inference environments; build automated training and inference pipelines for scalability and reproducibility.
  • •Monitor model drift and performance in production, establish feedback loops, and optimize inference latency and resource usage (e.g., quantization/distillation).

Key Requirements

  • •5+ years of experience in machine learning engineering with a track record of deploying models into production environments.
  • •Expert proficiency in Python and core ML/AI libraries including PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.
  • •Strong experience with transformer architectures (BERT, GPT, Llama) and NLP/GenAI frameworks such as Hugging Face and LangChain.
  • •Proficiency in MLOps practices and tools including Docker, Kubernetes, and experiment tracking with MLflow.
  • •Ability to design data preprocessing pipelines for both structured (SQL/tabular) and unstructured (text/PDF) data.
Experience:5+ years
Skills:CommunicationCollaborationAlgorithmic problem-solving
Languages:English
Tech Stack:PythonPyTorchTensorFlowScikit-learnPandasNumPyBERTGPTLlamaHugging FaceLangChainDockerKubernetesKubeflowMLflowRAGSQLPDF

Company Brief

AI71
AI71 builds generative AI and machine learning products and solutions, focusing on applying large language models and conversational AI to automate workflows, enhance decision-making, and deliver domain-specific AI applications for enterprise customers.
Industry: AI & Machine Learning
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