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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 SageMaker, EKS, Lambda, and AWS-native CI/CD tooling.
Key Responsibilities
AWS Platform Engineering
Design, deploy, and manage AWS foundational infrastructure:
o VPCs, Subnets, Route 53, NAT/Transit Gateway, PrivateLink
o Load Balancers (ALB/NLB), API Gateway
o S3, EFS, FSx, CloudFront, EventBridge
Manage compute and orchestration:
o 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 SageMaker:
o Data preparation
o Model training and hyperparameter tuning
o Model evaluation and registration
o 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:
o SageMaker Endpoints, EKS, or Lambda-based inference.
Integrate MLOps CI/CD with:
o CodePipeline, CodeBuild, GitHub Actions, or Jenkins.
Implement monitoring & drift detection:
o Model performance, data drift, concept drift, latency/SLA metrics.
Configure experiment tracking using SageMaker Experiments or MLflow.
Apply Responsible AI checks: explainability, fairness, compliance.
Automation & DevOps Integration
Build CI/CD pipelines for both platform and ML workloads:
o 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:
o 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, GuardDuty, 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:
o Terraform, CloudFormation, or CDK
o SageMaker, ML training & inference pipelines
o Containers: Docker, EKS, ECS
o CI/CD: CodePipeline, 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 (SageMaker Feature Store, Feast).
Familiarity with A/B testing and shadow deployments for ML.
FinOps exposure for ML compute optimization.
Certifications:
o AWS ML Engineer – Associate,
o AWS Solutions Architect,
o AWS DevOps Engineer Pro,
o Terraform Associate.
15 years full time education
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 SageMaker, EKS, Lambda, and AWS-native CI/CD tooling.
Key Responsibilities
AWS Platform Engineering
Design, deploy, and manage AWS foundational infrastructure:
o VPCs, Subnets, Route 53, NAT/Transit Gateway, PrivateLink
o Load Balancers (ALB/NLB), API Gateway
o S3, EFS, FSx, CloudFront, EventBridge
Manage compute and orchestration:
o 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 SageMaker:
o Data preparation
o Model training and hyperparameter tuning
o Model evaluation and registration
o 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:
o SageMaker Endpoints, EKS, or Lambda-based inference.
Integrate MLOps CI/CD with:
o CodePipeline, CodeBuild, GitHub Actions, or Jenkins.
Implement monitoring & drift detection:
o Model performance, data drift, concept drift, latency/SLA metrics.
Configure experiment tracking using SageMaker Experiments or MLflow.
Apply Responsible AI checks: explainability, fairness, compliance.
Automation & DevOps Integration
Build CI/CD pipelines for both platform and ML workloads:
o 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:
o 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, GuardDuty, 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:
o Terraform, CloudFormation, or CDK
o SageMaker, ML training & inference pipelines
o Containers: Docker, EKS, ECS
o CI/CD: CodePipeline, 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 (SageMaker Feature Store, Feast).
Familiarity with A/B testing and shadow deployments for ML.
FinOps exposure for ML compute optimization.
Certifications:
o AWS ML Engineer – Associate,
o AWS Solutions Architect,
o AWS DevOps Engineer Pro,
o 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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We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Accenture Chennai, Tamil Nadu, IND Office
Chennai, India
What you need to know about the Chennai Tech Scene
To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.
