Sr Officer-AI & ML Engineer

Department: Group B2B Customer & Business Operation

Role Purpose

• Developing and deploying cutting-edge machine learning and artificial intelligence solutions.
• Design, implement, and optimize machine learning models and AI systems that solve complex business problems and enable data-driven decision-making.
• Work closely with data scientists, software engineers, and other stakeholders to bring AI and ML solutions into production and improve business operations.

Scope of Work

Area of Responsibilities

Key Activities

Deliverables

Operational Delivery Excellence

• Deliver & lead project services to customer.
• Deliver services to customer as SoW successfully.

Strategic Cloud Delivery Management

• Develop and maintain a strategic roadmap for AI and Machine Learning enhancement.
• Conduct regular assessments of AI/ML and identify areas for improvement.

Client and Stakeholder Engagement

• Prepare and present performance reports to stakeholders.
• Gather client feedback and use it to improve service delivery.

Minimum Requirement

Qualification:

• 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).

Experience:

• 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.

Skill:

• 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.