Packaged/SaaS App Engineering Lead
Project Role Description : Own the design and evolution of packaged or SaaS solutions, overseeing configuration, integrations, and releases. Set platform standards to ensure stability, performance, and alignment with business and vendor constraints.
Must have skills : Kubernetes
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Senior AWS Full-Stack, DevOps and MLOps Engineer, a typical day involves developing Python-based cloud applications and APIs, designing AWS platforms, deploying containerized workloads on Amazon EKS, automating infrastructure and delivery pipelines, and building reliable MLOps capabilities. The role requires collaboration with application, data science, platform, security, architecture, and operations teams to deliver scalable, secure, and highly available solutions. The engineer combines strong AWS and Python development expertise with Kubernetes, DevOps, Infrastructure as Code, CI/CD, and end-to-end machine-learning lifecycle practices. Technical leadership, platform ownership, stakeholder alignment, mentoring, and continuous improvement are key aspects of the role.
Roles & Responsibilities:
Expected to be an SME, collaborate with multiple teams, and manage the team to perform effectively.
Responsible for technical and team-level decisions across AWS application development, DevOps, Kubernetes, Amazon EKS, and MLOps delivery.
Engage with application, data science, platform, security, architecture, and operations teams and contribute to key technical decisions.
Provide solutions to complex development, infrastructure, deployment, machine-learning lifecycle, performance, availability, and production problems across teams.
Design and develop Python-based backend services, REST APIs, serverless applications, automation utilities, data/ML services, and microservices on AWS.
Design, build, deploy, and operate containerized application and ML workloads using Docker, Kubernetes, Amazon EKS, Helm, and automated delivery pipelines.
Design and maintain secure, scalable, highly available, resilient, and cost-aware AWS cloud architectures.
Create and manage Infrastructure as Code using AWS CDK, Terraform, or AWS CloudFormation.
Design and maintain CI/CD pipelines using AWS CodePipeline, AWS CodeBuild, Jenkins, GitHub Actions, or equivalent tools.
Build MLOps pipelines for model training, validation, packaging, registry, deployment, monitoring, retraining, rollback, and governance.
Implement AWS and MLOps services using Amazon SageMaker, SageMaker Pipelines, Model Registry, endpoints, S3, ECR, Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, RDS, DynamoDB, and CloudWatch as applicable.
Apply cloud and Kubernetes security controls including IAM, RBAC, network policies, encryption, secrets management, image security, vulnerability remediation, and least-privilege access.
Implement monitoring, logging, tracing, alerting, model-performance monitoring, drift detection, incident response, backup, and disaster-recovery practices.
Lead troubleshooting, root-cause analysis, performance and cost optimization, release coordination, and production support.
Mentor junior team members and establish reusable automation, coding standards, infrastructure modules, deployment patterns, documentation, and engineering best practices.
Facilitate communication between technical and business stakeholders to align AWS and MLOps solutions with business objectives.
Professional & Technical Skills / Technical Expertise:
Must To Have Skills: Strong hands-on expertise in Python development, Amazon Web Services (AWS), Amazon EKS, Kubernetes, DevOps, and MLOps.
Strong Python software-development experience for backend APIs, microservices, serverless applications, AWS automation, data/ML services, infrastructure tooling, and operational utilities.
Strong hands-on knowledge of AWS services including IAM, VPC, EC2, S3, ECR, RDS, DynamoDB, Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, CloudWatch, Route 53, Elastic Load Balancing, and Auto Scaling.
Strong Kubernetes and Amazon EKS expertise covering cluster architecture, node groups, deployments, services, ingress, Helm, configuration, secrets, autoscaling, upgrades, RBAC, network security, observability, and troubleshooting.
Strong Docker expertise for container image design, secure builds, registries, runtime configuration, and deployment of application and ML workloads.
Strong MLOps expertise covering experiment tracking, model packaging and versioning, data and feature pipelines, automated training and validation, model registry, deployment, model monitoring, drift detection, retraining, rollback, and governance.
Hands-on experience with Amazon SageMaker, SageMaker Pipelines, Model Registry, real-time endpoints, batch inference, and integration of ML workflows with CI/CD.
Strong Infrastructure as Code experience using AWS CDK, Terraform, or AWS CloudFormation.
Strong CI/CD experience using AWS CodePipeline, AWS CodeBuild, Jenkins, GitHub Actions, Git, artifact repositories, automated testing, environment promotion, and release controls.
Experience building full-stack or backend cloud solutions by integrating Python services with web applications, REST APIs, databases, event-driven services, and AWS infrastructure.
Strong understanding of AWS networking, IAM, encryption, secrets management, cloud security, high availability, scalability, resilience, backup, and disaster recovery.
Experience with observability for applications, Kubernetes platforms, and ML workloads using Amazon CloudWatch, logs, metrics, traces, dashboards, and alerts.
Strong knowledge of Linux, shell scripting, API design, microservices, Agile, Scrum, DevOps, SRE principles, and SDLC methodologies.
Strong analytical, troubleshooting, communication, collaboration, mentoring, stakeholder-management, and technical-leadership skills.
Additional Information:
The candidate should have 10–12 years of overall experience in AWS cloud engineering and software development, with strong hands-on experience in Python, Amazon EKS, Kubernetes, DevOps, and MLOps.
The role is applicable for L9–L8 senior-level hiring.
The candidate should have 15 years of full-time education.
Location: Flexible across Accenture office locations in India.
Hyderabad
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