MLOps Lead Engineer

5+ years of experience in platform engineering, with a proven track record of designing, deploying, and managing scalable and secure cloud-based infrastructures, leveraging both Azure and AWS services.  Experience with Azure services such as Azure AI services, Azure Search, Azure ML, Databricks, Azure Kubernetes Service, and AWS services like AWS SageMaker, AWS Bedrock and AWS Lambda.  Exposure to Generative AI and Agentic AI ecosystems such as Azure OpenAI, Azure AI Foundry, Azure AI Hub, Bedrock, Anthropic Claude, OpenAI API, LlamaCloud, LangChain.  Understanding of token usage, LLM prompt injection risks, Jailbreak attempts and mitigation techniques.  Strong knowledge of governance, audit, observability, and compliance in cloud- based GenAI and ML ecosystems.  Should understand Azure AI Evaluation SDK and AI Red Teaming Prompt Security Scans  Good to have experience with code assistant tools like Github Copilot, Cursor and Claude Code  Expertise in Azure DevOps or AWS CodePipeline, including setting up and managing CI/CD pipelines.  Advanced experience with Azure Blob Storage, Cosmos DB, SQL, Key Vault,, AWS S3, DynamoDB, and AWS RDS etc and their integrations with AI services  Advanced understanding of networking concepts, including DNS management, load balancing, VPNs, and virtual networks (VNets).  Advanced understanding of security concepts, including IAM roles, identities, Azure policies, AWS SCPs.  Experience in Advanced Authentication and Authorization Concepts across various cloud providers and platforms  Must have experience with Azure Policy, AWS SCP, AWS IAM, audit logging, Azure RBAC etc.  Mastery of infrastructure-as-code tools such as Azure ARM / Bicep, Terraform, CloudFormation, or equivalent.  Proficiency in networking, DNS, load balancers, and cloud engineering services.  Knowledge in Python programming and AI/ML libraries (TensorFlow, PyTorch, Sci-Kit learn, Bash & PowerShell etc.).  Experience with containerization and orchestration tools such as Docker and Kubernetes.  Good to have knowledge about Azure Bot framework, APIM, Application Gateway. Also, knowledge about M365 offerings like M365 Copilot. AWS CDK, AWS Python(Boto3) SDK.  Experience with monitoring tools like Grafana, Prometheus, Application Insights, Log Analytics Workspaces, and Azure Monitor  Understanding of common database technologies for both OLTP and OLAP applications  Azure Services knowledge (Azure machine learning) o Experience in ML tooling Knowledge of Azure Machine Learning studio, Python SDK (v2),CLI (v2)) to monitor, retrain, and redeploy models. o Exposure to Azure Machine Learning model as Architect or model built from an open-source platform, such as Pytorch, TensorFlow, or scikit- learn. o Practical knowledge of how to build efficient end-to-end ML workflows o Understanding of machine learning & deep learning concepts and algorithms, various statistical techniques, and experimentation analysis workflows o Enable production models across the ML lifecycle o Implement CI/CD orchestration for data science pipelines o Understanding the production deployments and post-deployment model lifecycle management activities: drift monitoring, model retraining, and model technical evaluation business validation o Work with stakeholders to assist with ML pipeline -related technical issues and support modelling infrastructure needs.  Security and Access o Familiarity with Microsoft AD o Understanding of principle of least privilege and its application to projects (RBAC)  Testing o Understand and apply unit testing in day-to-day development work o Applied knowledge of integration testing as part of CI/CD process (ideally on ADO)  Infrastructure as Code o Understand the key concepts of IaC and some practical experience of application o Write code in languages such as Python or TypeScript to define cloud infrastructure as code  Specific to AWS: o AWS database services (such as RDS, DynamoDB, Redshift, Aurora) o AWS compute services and storage (such as EC2 – including scaling, EBS, EFS) o AWS serverless technology (including Lambda, SQS, SNS, EventBridge and Step functions) o AWS KMS o AWS container services (including ECR) o AWS CloudFormation and the CDK  Specific to AWS: o Azure database services (including Cosmos DB, Azure SQL Serverless) o Azure compute services (VM, VM Scale Sets) o Azure serverless technology (i.e. Functions, Event Grid/Hub, Queue Storage, Service Bus) o Azure container services (including ACR / AKS) o Azure Resource Manager (ARM) / BICEP o Azure Key Vault o Azure Machine Learning o Azure Data Lake Storage