Build, train, and deploy advanced machine learning models, including regression, classification, clustering, and recommendation algorithms
Implement NLP, deep learning, and computer vision solutions as required to support customer-centric applications and predictive analytics
Apply knowledge of large language models (LLMs) to develop conversational AI and recommendation systems for customer engagement
Design end-to-end ML/AI pipelines that support the continuous integration and deployment of machine learning models into production environments
Leverage MLOps best practices for model versioning, retraining, performance monitoring, and scalability
Ensure models are optimized for latency, accuracy, and scalability by deploying on cloud platforms such as AWS, GCP, or Azure
Collaborate with data engineering teams to design and optimize ETL/ELT pipelines for AI-specific data needs
Engineer and preprocess large, complex datasets from various sources to ensure model robustness, accuracy, and generalizability
Perform predictive analytics to support business strategies, creating foresight-driven models that enhance customer experiences and drive revenue growth
Partner with business stakeholders to identify areas where ML/AI can drive value and translate these into actionable AI projects
Document processes, code, and models for knowledge sharing and team scalability

5+ years of hands-on experience in machine learning, AI engineering, or data science
Extensive experience working with large datasets, building and fine-tuning ML models, and deploying on cloud platforms
Demonstrated expertise in deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with NLP and large language models
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
Master’s or Ph.D. in Machine Learning, AI, or a similar field is strongly preferred for a senior role
Proficiency in Python and ML libraries (e.g., scikit-learn, Keras, Hugging Face)
Strong experience in cloud-based ML services (e.g., AWS SageMaker, GCP AI Platform, Azure ML)
Knowledge of MLOps tools and practices (e.g., MLflow, Airflow, Docker, Kubernetes)

Senior Machine Learning Engineer

Key skills