Machine Learning Engineer

2 weeks ago

Dubai, Dubai, United Arab Emirates dubizzle Full time
Roles and responsibilities

As the Machine Learning Engineer, youwill be participating in exciting projects covering the end-to-endData Science lifecycle - from raw data cleaning and explorationwith primary and third-party systems, through advancedstate-of-the-art data visualization and Machine learningdevelopment. You will work in a modern cloud-based data warehousingenvironment hosting Machine Learning models alongside a team ofdiverse, intense and interesting co-workers. You will liaise withother departments - such as product & tech, the corebusiness verticals, trust & safety, finance and others - toenable them to be successful.

Inthis role, youwill:

  • Work onregression and classification problems on tabular, textual andimage data
  • Work on forecasting, anomalydetection and time-series analysis
  • Buildrecommendation engines
  • Work on GPT-basedapplications using stock models for various business usecases
  • Query large datasets in AWS Redshift toextract the necessary data that will feed MLmodels
  • Perform data exploration to findpatterns in the data and understand the state and quality of thedata available
  • Utilize Python code foranalyzing data and building statistical models to solve specificbusiness problems
  • Evaluate ML models and finetune model parameters considering the business problembehind
  • Collaborate with senior peers to DeployML models into production that work as standalone dataservices
  • Build customer-facing reporting toolsto provide insights and metrics which track systemperformance
  • Participate in the off-hours oncall stability rota to support live MLmodels
  • Own at least one ML product that is inproduction

Requirements

  • Master's degree in AI,Statistics, Math, Operations Research, Engineering, ComputerScience, or a related quantitative field
  • 2+years of working experience in MachineLearning
  • Experience with AWS is aplus
  • Knowledge in Statistical modelling andmaths
  • Intermediate knowledge ofPython's ML stack: Pandas, Matplotlib, Sklearn,Tensorflow
  • Intermediate knowledge of machinelearning algorithms such as Linear regression, Gradient boostedtrees, Neural networks
  • Basic knowledge of SQL,and visualization tools such as Periscope with experience inhandling large datasets
  • Basic knowledge ofstatistical analysis and A/B testing
  • Excellentverbal and written communication
  • Strong problemsolving skills
  • Analytical thinking; Conceptualthinking Detail-oriented; BusinessAcumen
  • Entrepreneurial spirit and ability tothink creatively; highly-driven and self-motivated; strongcuriosity and strive for continuous learning
Desired candidate profile

. Developing andImplementing Machine LearningModels

  • ModelDevelopment: Design and implement machine learningmodels to address specific business problems (e.g., classification,regression,clustering).
  • AlgorithmSelection: Choose appropriate algorithms based onthe problem requirements, such as supervised, unsupervised, orreinforcement learning.
  • ModelTraining and Evaluation: Train models on largedatasets and evaluate their performance using metrics likeaccuracy, precision, recall, F1-score, or AUC (depending on thetask).
  • ModelOptimization: Tune hyperparameters, adjustalgorithms, and experiment with different architectures to improvemodel performance.

2.Data Processing and FeatureEngineering

  • DataCleaning: Preprocess raw data, handle missingvalues, outliers, and ensure that the data is clean and suitablefor machine learning.
  • FeatureEngineering: Extract relevant features from rawdata, transforming it into a format suitable for modeling (e.g.,scaling, encoding, dimensionalityreduction).
  • DataIntegration: Combine data from different sources andensure proper data flow into the machine learningpipeline.

3. ModelDeployment andIntegration

  • DeployingModels: Work with software engineers to deploymachine learning models into productionenvironments.
  • APIDevelopment: Develop APIs for integrating machinelearning models into larger systems or applications for real-timeor batchinference.
  • Scalability:Ensure that machine learning models can handle large-scale data andcan be efficiently used in production environments (e.g., usingdistributed computing, cloud services like AWS, GCP, orAzure).
  • Monitoring andMaintenance: Continuously monitor model performancein production, detecting issues like data drift or modeldegradation, and retrain models whennecessary.

4.Collaboration with Cross-FunctionalTeams

  • Workingwith Data Scientists: Collaborate with datascientists to understand the problem, select the right algorithms,and develop experimentalmodels.
  • Working with SoftwareEngineers: Work with software engineers to integratemachine learning models into software products orservices.
  • BusinessStakeholders: Translate business requirements intotechnical solutions and communicate findings to non-technicalstakeholders.
Key Skills
Machine and Equipment,MachineCapacity,Machine Management
Employment Type: Full-time
Department / FunctionalArea: Engineering
Experience: years
Gender: Male
Vacancy: 1

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