Machine Learning Operations (MLOps) Engineer (AWS/Azure)

MLOps Engineer (AWS/Azure)

Role Type:

Location:

Hours:

Start Date:

End Date:

Day Rate:

Process:

1.     ICS first Interview (30 minutes).

2.    CV shared with the client.

3.    Meet the client and complete a technical interview/assessment.

Context of the client

• The client is a global energy company undergoing a significant transformation to support the energy transition.
• We work within their Customers & Products (C&P) division, serving both B2C and B2B customers across key markets, including the UK, US, Germany, Spain, and Poland.
• This business unit includes mobility (fuel and EV), convenience retail, and loyalty.

Context of the ICS:

At The Institute of Clever Stuff (ICS), we don’t just solve problems... we revolutionise results. Our mission is to empower a new generation of Future Makers today, to revolutionise results and create a better tomorrow. Our vision is to pioneer a better future together. We are a consulting firm with a difference, powered by AI, driving world-leading results from data and change. We partner with visionary organisations to solve their toughest challenges, drive transformation, and deliver high-impact results.

fortu.ai

Essential Requirements

• 9+ years

   of relevant professional experience, including 

  5+ years

   in platform engineering, designing, deploying, and managing scalable, secure cloud infrastructure across both Azure and AWS.
• Strong grounding in 

  governance, audit, observability, and compliance

   for cloud-based GenAI/ML ecosystems.
• Proven experience setting up and managing 

  CI/CD

   using 

  Azure DevOps

   or 

  AWS CodePipeline

  .
• Proficiency with 

  infrastructure‑as‑code

   (ARM/Bicep, Terraform, CloudFormation, CDK) and 

  containerisation

  (Docker, Kubernetes).
• Advanced understanding of 

  networking

   (DNS, load balancing, VPNs, VNets/VPCs) and 

  security

   (IAM, RBAC, policies, SCPs).
• Solid programming skills in 

  Python

   plus scripting (

  Bash

  , 

  PowerShell

  ); familiarity with mainstream 

  AI/ML libraries

   (TensorFlow, PyTorch, scikit‑learn).
• Experience with 

  cloud data stores

   and key management (Azure Blob, Cosmos DB, SQL, Key Vault; AWS S3, DynamoDB, RDS/KMS) and their integrations with AI services.

Core Technical Expertise (Must Have):

• Azure & AWS ML/AI services:

   Azure ML, Azure AI Services, Azure AI Search; AWS SageMaker, AWS Bedrock, AWS Lambda.

• GenAI & Agentic ecosystems:

   Exposure to Generative AI and Agentic AI ecosystems, such as Azure OpenAI, Azure AI Foundry/Hub, Bedrock, Anthropic Claude, OpenAI API, LlamaCloud, LangChain.

• Security & identity:

   Azure Policy, Azure RBAC, AWS IAM, AWS SCPs; audit logging; least‑privilege design.

• IaC & platform automation:

   ARM/Bicep, Terraform, CloudFormation, CDK.

• DevOps/CI‑CD:

   Azure DevOps or AWS CodePipeline; integration and delivery for data science and ML workflows.

• Data & storage:

   Azure Blob/Cosmos/SQL/Key Vault; AWS S3/DynamoDB/RDS; understanding of OLTP and OLAP patterns.

• Containers & orchestration:

   Docker and Kubernetes (including AKS/EKS patterns and ECR/ACR usage).

• Monitoring & observability:

   Grafana, Prometheus, Azure Monitor, Application Insights, Log Analytics Workspaces.

• Networking:

   DNS management, load balancing, VPNs, virtual networks (VNets/VPCs).

• Testing:

   Unit and integration testing as part of CI/CD (ideally on Azure DevOps).

• ML tooling:

   Azure ML Studio, Python SDK (v2), CLI (v2) for monitoring, retraining, and redeployment.

• AI safety & evaluation:

   Token usage comprehension; prompt injection/jailbreak risks and mitigations; Azure AI Evaluation SDK; AI red‑teaming prompt security scans.

Working Methods:

• Agile, sprint‑based delivery with 

  Azure DevOps

   (boards, repos, pipelines).
• Strong DevOps and 

  CI/CD

   pipeline management across environments.
• Close collaboration with Data Scientists, Data Analysts, Software Engineers, and platform teams.
• Clear documentation and communication suited to distributed teams.
• Stakeholder engagement to troubleshoot ML pipeline issues and support modelling infrastructure needs.

Beneficial Experience:

• Developer productivity:

   GitHub Copilot, Cursor, Claude Code.

• Microsoft/Azure services:

   Azure Bot Framework, API Management, Application Gateway, 

  M365 Copilot

  .

• AWS SDKs & tooling:

   

  Boto3

  , AWS CDK.

• Notebooks & experimentation:

   Jupyter Notebook.

• ML frameworks:

   PyTorch, TensorFlow, scikit‑learn; practical E2E ML workflow design.

Responsibilities

Platform & Infrastructure

• Design, deploy, and manage scalable and secure cloud infrastructure across Azure and AWS using IaC (ARM/Bicep/Terraform/CloudFormation/CDK).
• Implement core networking (DNS, load balancing, VPNs, VNets/VPCs) and platform services for reliability and performance.
• Build and operate container platforms (Docker, Kubernetes; ACR/AKS and ECR/EKS patterns).
• Set up comprehensive monitoring and logging (Grafana, Prometheus, Azure Monitor, Application Insights, Log Analytics).

Security & Compliance:

• Apply the

  principle of least privilege

   across cloud platforms (Azure RBAC, AWS IAM) and enforce policy (Azure Policy, AWS SCPs).
• Enable audit logging and controls appropriate for GenAI/ML workloads.
• Manage secrets and keys with 

  Azure Key Vault

   and 

  AWS KMS

  .

CI/CD & Testing

• Implement CI/CD for data science/ML pipelines with Azure DevOps or AWS CodePipeline.
• Embed robust unit and integration testing in the pipeline; champion code quality and operational readiness.

Infrastructure as Code (IaC)

• Define and evolve cloud resources as code; review and maintain standards, patterns, and reusable modules.
• Use Python or TypeScript where appropriate to codify infrastructure definitions.

Cloud Services (AWS & Azure)

• AWS:

   RDS, DynamoDB, Redshift, Aurora; EC2 (scaling), EBS/EFS; serverless (Lambda, SQS, SNS, EventBridge, Step Functions); containers (ECR); Bedrock; SageMaker; CloudFormation (CDK); KMS.

• Azure:

   Cosmos DB, Azure SQL (including Serverless); compute (VMs, Scale Sets); serverless (Functions, Event Grid/Hub, Queue Storage, Service Bus); container services (ACR/AKS); Azure Resource Manager (ARM)/Bicep; Azure Key Vault; Azure Machine Learning; Azure Data Lake Storage.

MLOps & Model Lifecycle

• Enable production models across the ML lifecycle (deployment, monitoring for drift, retraining, technical evaluation, and business validation).
• Implement CI/CD orchestration for data science pipelines and support model governance.
• Collaborate with stakeholders to resolve ML pipeline issues and evolve the modelling platform.