Senior Machine Learning Engineer
Senior Machine Learning Engineer
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1 Vacancy
Job Description
Senior Machine Learning Engineer 3 Year Contract
Key Responsibilities:
- Model Development & Optimization: Design develop and optimize machine learning models for realworld applications ensuring high accuracy scalability and efficiency.
- ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS Azure GCP) and containerization technologies (Docker Kubernetes).
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Feature Engineering & Data Processing: Collaborate with data engineers to preprocess clean and transform large datasets for training and inference.
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Productionization: Deploy ML models into production monitor performance and continuously improve them through A/B testing and retraining.
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Collaboration: Work closely with crossfunctional teams including software engineers product managers and business stakeholders to align ML solutions with business objectives.
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MLOps & Automation: Implement MLOps best practices automate model training and deployment and ensure reproducibility.
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Performance Monitoring: Develop and maintain monitoring tools to track model performance drift and reliability in production.
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Research & Innovation: Stay updated with the latest trends and advancements in AI/ML and integrate cuttingedge research into business solutions.
Required Qualifications & Skills:
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Education: Bachelors or Masters degree in Computer Science Data Science Machine Learning or a related field. A Ph.D. is a plus.
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Experience: Minimum 5 years of experience in machine learning deep learning and AI model deployment in production environments.
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Programming: Strong proficiency in Python with experience in libraries like TensorFlow PyTorch Scikitlearn Pandas and NumPy.
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Cloud & Infrastructure: Handson experience with cloud services (AWS GCP Azure) and MLOps tools like Kubeflow MLflow or SageMaker.
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Big Data & Databases: Experience with Spark Hadoop SQL and NoSQL databases for handling largescale datasets.
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DevOps & CI/CD: Familiarity with Git Docker Kubernetes and CI/CD pipelines for ML model deployment.
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Algorithm Development: Strong knowledge of ML algorithms deep learning architectures (CNNs RNNs Transformers) and optimization techniques.
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ProblemSolving: Strong analytical and problemsolving skills with the ability to design innovative ML solutions for complex business challenges.
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Excellent Communication: Ability to explain technical concepts to nontechnical stakeholders and document ML processes effectively.
Preferred Qualifications:
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Experience with NLP Computer Vision or Reinforcement Learning.
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Handson experience with AutoML hyperparameter tuning and model interpretability.
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Experience with realtime ML applications and edge AI.
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Contributions to opensource ML frameworks or research publications.
Employment Type
Full Time
