Roles and Responsibilities - Lead the design and development of AI and ML applications across cloud or on-prem environments - Build, maintain, and optimize MLOps pipelines for model training, deployment, monitoring, and retraining - Integrate Generative AI models into enterprise applications and workflows - Apply deep learning, neural networks, image processing, or conversational AI where needed - Act as the SME for AI/ML engineering practices, standards, and solution architecture - Collaborate with cross-functional teams to support end-to-end delivery - Ensure production systems meet reliability, scalability, and performance standards - Guide and mentor junior engineers and contribute to technical leadership - Support data engineering pipelines, data preparation, and model operationalization Required Qualifications - 5+ years of experience in AI/ML engineering, including 3+ years in MLOps - Strong hands-on experience with AWS SageMaker, Databricks, and CI/CD tools - Proficiency in Python; experience with R or SAS is an advantage - Experience deploying, monitoring, and optimizing ML models in production environments - Knowledge of Docker, Kubernetes, REST APIs, and JSON processing - Familiarity with big-data ecosystems such as Spark or Hadoop - Solid understanding of ETL processes, data modeling, and data engineering practices - Experience with cloud platforms (AWS, Azure, or GCP) and ML-focused architectures - Strong version control, dependency management, and automation skills