Project Role: Cloud Platform Engineer

Project Role Description: Designs, builds, tests, and deploys cloud application solutions that integrate cloud and non-cloud infrastructure. Can deploy infrastructure and platform environments, creates a proof of architecture to test architecture viability, security and performance.

Must have skills: AWS CloudFormation

Good to have skills: NA

Minimum 7.5 year(s) of experience is required

Educational Qualification: 15 years full time education

Job Summary:

We are seeking a highly skilled AWS Platform & MLOps Engineer with strong expertise in AWS cloud infrastructure, automation, and operationalizing machine learning (ML) workloads. The role focuses on building secure, scalable AWS platforms, implementing IaC-driven deployments, enabling end to end MLOps workflows, and ensuring reliable model delivery using services like Amazon Sage Maker, EKS, Lambda, and AWS-native CI/CD tooling.

Key Responsibilities:

• AWS Platform Engineering

• Design, deploy, and manage AWS foundational infrastructure:

• VPCs, Subnets, Route 53, NAT/Transit Gateway, Private Link

  • Load Balancers (ALB/NLB), API Gateway
  • S3, EFS, FSx, CloudFront, Event Bridge

• Manage compute and orchestration:

• EC2, EKS, ECS, Lambda

• Implement hybrid connectivity (VPN, Direct Connect).

• Enforce best practices for high availability, DR, and platform resiliency.

• Optimize cloud resources through scaling, right sizing, and cost governance.

• Infrastructure as Code (IaC)

• Build reusable IaC modules using Terraform (preferred) or AWS CloudFormation/CDK.

• Automate provisioning of networking, compute, storage, security, and ML services.

• Use IaC scanning and policy controls (Checkov, OPA, AWS Config Rules).

• MLOps Engineering (AWS)

• Develop and maintain end to end ML pipelines using Amazon Sage Maker:

• Data preparation

  • Model training and hyperparameter tuning
  • Model evaluation and registration
  • Model deployment to real time or batch endpoints

• Manage Model Registry, track versions, and promote models across stages (Dev Staging Prod).

• Deploy ML models on:

• Sage Maker Endpoints, EKS, or Lambda-based inference.

• Integrate MLOps CI/CD with:

• Code Pipeline, CodeBuild, GitHub Actions, or Jenkins.

• Implement monitoring & drift detection:

• Model performance, data drift, concept drift, latency/SLA metrics.

• Configure experiment tracking using Sage Maker Experiments or MLflow.

• Apply Responsible AI checks: explainability, fairness, compliance.

• Automation & DevOps Integration

• Build CI/CD pipelines for both platform and ML workloads:

• Automated training, packaging, validation, and deployment.

• Manage containerized pipelines (Docker, ECR, EKS).

• Implement versioned artifact workflows (models, features, images, IaC templates).

• Security, Identity & Governance

• Implement security best practices:

• IAM roles/policies, STS, SCPs, KMS, Secrets Manager, parameter store.

• Apply governance through AWS Organizations, Control Tower, and Landing Zone patterns.

• Protect endpoints and ML services with VPC isolation, network policies, encryption, and private access.

• Monitoring, Logging & Observability

• Configure CloudWatch, CloudTrail, Guard Duty, Inspector for platform and ML visibility.

• Set up dashboards, alerts, and automated remediation (Lambda/SSM).

• Ensure reliability and SRE excellence (SLIs, SLOs, error budgets).

• Required Skills

• 4–10 years of AWS cloud engineering experience, including platform operations.

• Strong experience in:

• Terraform, CloudFormation, or CDK

  • Sage Maker, ML training & inference pipelines
  • Containers: Docker, EKS, ECS
  • CI/CD: Code Pipeline, Jenkins, GitHub Actions

• Proficiency in Python (must have for ML pipeline automation).

• Strong understanding of AWS networking, IAM, and security architecture.

• Experience with monitoring tools (CloudWatch, Prometheus/Grafana optional).

• Nice to Have

• Experience with Databricks, EMR, Glue, or Athena for data engineering workflows.

• Knowledge of feature stores (Sage Maker Feature Store, Feast).

• Familiarity with A/B testing and shadow deployments for ML.

• Fin Ops exposure for ML compute optimization.

• Certifications:

• AWS ML Engineer – Associate,

  • AWS Solutions Architect,
  • AWS DevOps Engineer Pro,
  • Terraform Associate.

15 years full time education

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.
Visit us at www.accenture.com

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