Machine Learning Engineer

Experience - 5 +

Location - Remote

Responsibilities

• :Design, implement, and manage scalable cloud-based AI/ML infrastructure across Azure and AWS
• .Drive end-to-end MLOps lifecycle model deployment, monitoring, retraining, and governance
• .Enable GenAI and Agentic AI platforms leveraging Azure OpenAI, Bedrock, Anthropic Claude, LangChain, etc
• .Implement CI/CD pipelines using Azure DevOps or AWS CodePipeline
• .Ensure security, observability, and compliance across ML and GenAI ecosystems
• .Manage infrastructure automation via Terraform, Bicep, CloudFormation, or similar IaC tools
• .Collaborate with data science and engineering teams to optimise ML workflows, data pipelines, and API integrations
• .Implement monitoring and alerting using Grafana, Prometheus, Azure Monitor, and Application Insights
• .Oversee networking, identity management, and role-based access controls (IAM, RBAC) across clouds
• .Support model lifecycle management drift monitoring, retraining, technical evaluation, and business validation

.Requirement

• s:Azure: Azure ML, Azure AI Services, Azure OpenAI, Azure Kubernetes Service (AKS), Databricks, Azure Search, Azure Blob, Cosmos DB, Azure SQL, Azure Functions, Azure Event Hub, Azure Resource Manager (ARM), Bice
• p.AWS: SageMaker, Bedrock, Lambda, DynamoDB, S3 RDS, Redshift, ECR, CloudFormation, CDK, KMS, EventBridge, Step Function

s.AI/ML and Programmi

• ng:Hands-on in Python, with exposure to TensorFlow, PyTorch, scikit-lea
• rn.Understanding of LLM tokenisation, prompt injection risks, jailbreak prevention, and AI safety techniqu
• es.Familiarity with LangChain, LlamaCloud, AI Foundry, and related framewor
• ks.Experience in model monitoring, retraining, and evaluation workflo

ws.DevOps and Infrastruct

• ure:Expertise in CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure automat
• ion.Strong in governance, audit logging, security policies (Azure Policy, AWS SCP, I
• AM).Deep understanding of networking, DNS, load balancers, VNets/VPCs, V
• PNs.Skilled in IaC tools - Terraform, Bicep, ARM, CloudFormat

ion.Monitoring and Observabi

• lity:Experience with Grafana, Prometheus, Application Insights, Log Analytics Workspaces, and Azure Mon

itor.Security and Access Manag

• ement:Understanding of Microsoft AD, least privilege principles, IAM, and

RBAC.Testing and Auto

• mation:Familiarity with unit testing and integration testing in CI/CD workflows (preferably Azure D

evOps).Good

• to have:Experience with Azure Bot Framework, M365 Copilot, a
• nd APIM.Exposure to code assistants such as GitHub Copilot, Cursor, and Clau
• de Code.Knowledge of Boto3 SDK (AWS Python) and TypeScript

for IaC.Preferred Ba

• ckground:Strong background in cloud infrastructure engineering and machine learning op
• erations.Proven ability to lead cross-functional teams and implement AI governance
• at scale.Excellent problem-solving, communication, and documentatio