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Sr.Officer AI/ML Engineer

Salary undisclosed

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Responsibilities

  • Deliver & lead project services to customer
  • Give consultation to team as expert of AI and ML Engineer
  • Deliver services to customer as SoW successfully
  • Develop and maintain a strategic roadmap for AI and Machine Learning enhancement
  • Conduct regular assessments of AI/ML nd identify areas for improvement.
  • Roadmap for AI and ML enhancements.
  • Assessment and improvement plan reports
  • Prepare and present performance reports to stakeholders.
  • Gather client feedback and use it to improve service delivery
  • Facilitate knowledge sharing among team members.
  • Team coaching & sharing knowledge.

Requirements

  • Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
  • Published papers or contributions to AI/ML research.
  • Experience with reinforcement learning or generative models.
  • Familiarity with explainability and interpretability frameworks for machine learning models (e.g., SHAP, LIME).
  • Previous experience working with large-scale datasets or in a cloud-based environment (AWS, GCP, Azure).
  • 3+ years of experience in machine learning, AI, or data science roles, with hands-on experience developing and deploying models in production environments.
  • Strong experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar tools.
  • Solid understanding of data structures, algorithms, and computer science fundamentals.
  • Experience with deep learning techniques (CNNs, RNNs, GANs, etc.) and natural language processing (NLP) is highly desirable.
  • Experience with cloud platforms (AWS, Azure, GCP) for deploying AI/ML models is a plus.
  • Familiarity with model deployment tools such as Docker, Kubernetes, or MLOps platforms is a plus.
  • Proficiency in programming languages like Python, R, or Java, with a focus on data science and AI libraries (e.g., NumPy, pandas, Matplotlib).
  • Hands-on experience with databases (SQL, NoSQL) and big data technologies (e.g., Hadoop, Spark).
  • Understanding of version control systems like Git for collaborative development.
  • Familiarity with cloud infrastructure (AWS SageMaker, Google AI Platform, etc.) and containerization technologies (Docker, Kubernetes) is a plus.
  • Strong understanding of AI ethics and responsible AI practices.
  • Design, implement, and train machine learning and deep learning models to solve real-world problems, including supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP).
  • Develop algorithms and techniques for predictive analytics, recommendation systems, classification, regression, and anomaly detection.
  • Use frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn for model development and experimentation.
  • Clean, preprocess, and analyze large datasets to extract meaningful features for machine learning models.
  • Implement data augmentation, feature extraction, and transformation techniques to improve model accuracy.
  • Collaborate with data engineers to ensure that the data pipeline supports efficient feature engineering and model training.
  • Evaluate the performance of machine learning models using standard metrics (e.g., accuracy, precision, recall, AUC, F1 score) and validate models with real-world data.
  • Optimize models for speed, scalability, and accuracy, including hyperparameter tuning, cross-validation, and using advanced techniques like ensemble learning or neural architecture search.
  • Continuously improve and fine-tune models based on feedback and new data.
  • Monitor and maintain the performance of deployed machine learning models in production.
  • Develop tools and processes for model versioning, model retraining, and monitoring model drift.
  • Address model degradation and ensure that models are continuously improved as new data becomes available.
  • Document machine learning workflows, model architectures, and codebases to ensure reproducibility and transparency.
  • Create training materials and provide knowledge-sharing sessions to empower other teams to leverage AI/ML capabilities.
  • Ability to collaborate effectively with cross-functional teams, including developers, IT operations, and business stakeholders.

Responsibilities

  • Deliver & lead project services to customer
  • Give consultation to team as expert of AI and ML Engineer
  • Deliver services to customer as SoW successfully
  • Develop and maintain a strategic roadmap for AI and Machine Learning enhancement
  • Conduct regular assessments of AI/ML nd identify areas for improvement.
  • Roadmap for AI and ML enhancements.
  • Assessment and improvement plan reports
  • Prepare and present performance reports to stakeholders.
  • Gather client feedback and use it to improve service delivery
  • Facilitate knowledge sharing among team members.
  • Team coaching & sharing knowledge.

Requirements

  • Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
  • Published papers or contributions to AI/ML research.
  • Experience with reinforcement learning or generative models.
  • Familiarity with explainability and interpretability frameworks for machine learning models (e.g., SHAP, LIME).
  • Previous experience working with large-scale datasets or in a cloud-based environment (AWS, GCP, Azure).
  • 3+ years of experience in machine learning, AI, or data science roles, with hands-on experience developing and deploying models in production environments.
  • Strong experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar tools.
  • Solid understanding of data structures, algorithms, and computer science fundamentals.
  • Experience with deep learning techniques (CNNs, RNNs, GANs, etc.) and natural language processing (NLP) is highly desirable.
  • Experience with cloud platforms (AWS, Azure, GCP) for deploying AI/ML models is a plus.
  • Familiarity with model deployment tools such as Docker, Kubernetes, or MLOps platforms is a plus.
  • Proficiency in programming languages like Python, R, or Java, with a focus on data science and AI libraries (e.g., NumPy, pandas, Matplotlib).
  • Hands-on experience with databases (SQL, NoSQL) and big data technologies (e.g., Hadoop, Spark).
  • Understanding of version control systems like Git for collaborative development.
  • Familiarity with cloud infrastructure (AWS SageMaker, Google AI Platform, etc.) and containerization technologies (Docker, Kubernetes) is a plus.
  • Strong understanding of AI ethics and responsible AI practices.
  • Design, implement, and train machine learning and deep learning models to solve real-world problems, including supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP).
  • Develop algorithms and techniques for predictive analytics, recommendation systems, classification, regression, and anomaly detection.
  • Use frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn for model development and experimentation.
  • Clean, preprocess, and analyze large datasets to extract meaningful features for machine learning models.
  • Implement data augmentation, feature extraction, and transformation techniques to improve model accuracy.
  • Collaborate with data engineers to ensure that the data pipeline supports efficient feature engineering and model training.
  • Evaluate the performance of machine learning models using standard metrics (e.g., accuracy, precision, recall, AUC, F1 score) and validate models with real-world data.
  • Optimize models for speed, scalability, and accuracy, including hyperparameter tuning, cross-validation, and using advanced techniques like ensemble learning or neural architecture search.
  • Continuously improve and fine-tune models based on feedback and new data.
  • Monitor and maintain the performance of deployed machine learning models in production.
  • Develop tools and processes for model versioning, model retraining, and monitoring model drift.
  • Address model degradation and ensure that models are continuously improved as new data becomes available.
  • Document machine learning workflows, model architectures, and codebases to ensure reproducibility and transparency.
  • Create training materials and provide knowledge-sharing sessions to empower other teams to leverage AI/ML capabilities.
  • Ability to collaborate effectively with cross-functional teams, including developers, IT operations, and business stakeholders.