Also required is: 5 years of experience: with ML-Based Application Development & API Integration to build software features that rely on machine-learning models and to integrate those models into applications through APIs so the system can send data and receive predictions automatically; convert research-grade ML models into stable, scalable services that other applications and users can access reliably; building and deploying machine learning-based applications, including the design and integration of ML APIs and RESTful for scalable, production-grade systems; with data engineering & ETL Pipelines to design automated workflows that extract data from multiple sources, transform it into usable formats, and load it into databases or ML systems, ensuring that large volumes of structured and unstructured data are cleaned, validated, and delivered reliably for analytical and machine-learning purposes; designing and maintaining ETL workflows for large-scale data processing, including structured and unstructured data; utilizing database & Vector Store Technologies; using SQL/NoSQL databases and/or vector databases such as Solr, Elasticsearch, or Pinecone; with Cloud Infrastructure, Deployment Automation & Containerization to deploy and operate applications on cloud platforms using automated tools to ensure systems are scalable, consistent, and easy to maintain; deploying applications on cloud platforms including AWS, Azure, and GCP, using infrastructure-as-code tools such as Terraform, and containerization technologies including Docker; utilizing orchestration tools such as Kubernetes to automatically manage, deploy, and scale containerized applications across multiple servers to ensure applications remain available, recover quickly from failures, and can be updated or rolled back safely without interrupting service; with MLOps & Model Deployment; and with MLOps practices and deploying ML models using frameworks such as KubeRay, Triton Inference Server, or similar orchestration tools.