Senior AI engineer

Job Summary The Senior AI Engineer is responsible for designing and delivering production-grade AI/ML systems while establishing MLOps standards and engineering best practices. This role combines hands-on model development with technical leadership, driving scalable AI solutions and collaborating with cross-functional teams to align technology with business objectives. Key Responsibilities Design and own end-to-end AI/ML architecture, including data ingestion, feature engineering, model training, deployment, and monitoring Establish and maintain MLOps practices such as model versioning, CI/CD pipelines, automated retraining, and monitoring Define engineering standards for code quality, testing, documentation, and peer reviews Evaluate and recommend tools, frameworks, and platforms across cloud and hybrid environments Design and develop advanced AI/ML models including generative AI, NLP, and deep learning systems Build and optimize models for forecasting, classification, anomaly detection, and recommendation use cases Develop and maintain data pipelines and feature stores for reliable model training Ensure scalability, performance, and reliability of models in production environments Build AI solutions for use cases such as anomaly detection, predictive maintenance, and demand forecasting Develop models for customer analytics including churn prediction and recommendation systems Design AI-driven solutions for operational optimization such as scheduling and routing Mentor and guide team members through code reviews and architectural discussions Collaborate with stakeholders to translate business requirements into technical solutions Participate in vendor and platform evaluations through technical assessments and proof-of-concept development Required Qualifications 3+ years of experience in machine learning, AI engineering, or applied data science, with at least 2 years in a senior or lead role Proven experience deploying AI/ML models into production environments at scale Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn Experience designing and managing MLOps pipelines including CI/CD and monitoring Experience with cloud platforms such as AWS or Azure and hybrid environments Strong understanding of classical machine learning and modern generative AI techniques Experience with data pipeline tools such as Spark, Airflow, or similar Strong analytical, problem-solving, and communication skills Preferred Qualifications Experience working on telecom, network analytics, or customer experience use cases Experience with large language models and advanced NLP techniques Experience mentoring or leading technical teams Experience with feature stores and advanced data engineering practices