MLOps Engineer

Key Responsibilities Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio). LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data f

Total of 3-6 years of experience in managing machine learning projects end-to-end, with the last 18 months focused on MLOps Strong programming skills, preferably in languages like Python, Java, or Scala Proficiency in machine learning libraries and frameworks, such as TensorFlow, PyTorch, or scikit-learn Experience with containerization technologies, like Docker and Kubernetes Familiarity with ML model deployment tools, such as MLflow or Kubeflow Working experience in the Azure cloud platform Au

Role Overview This is a high-stakes, execution-focused role within the Transformation Office. We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives. Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services. You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud envir

We are seeking an experienced MLOps Engineer to join our DevOps and Infrastructure Engineering team. This role is crucial for operationalizing, scaling, and maintaining our Artificial Intelligence (AI) and Machine Learning (ML) applications. The successful candidate will leverage their expertise to ensure seamless, scalable, and reliable deployment and management of AI/ML models, working closely with data scientists and ML engineers. This position requires strong proficiency in Python, hands-on