Tech S and T-Network Engineer Senior-GDSN02
Ernst and Young1 day ago
Bengaluru, IndiaMid Level / Senior
Responsibilities
- Build and maintain secure CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, Azure DevOps, and CircleCI.
- Implement DevSecOps practices including SAST/DAST, container scanning, secret scanning, SBOM automation, dependency management, and policy-as-code.
- Deploy and manage containerized workloads on Kubernetes using Docker, Podman, Helm, and Kustomize.
- Develop MLOps workflows for model training, packaging, deployment, and serving using MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI, KServe, Seldon Core, BentoML, Ray Serve, or Triton Inference Server.
- Build RAG and AI integration pipelines using LangChain, LlamaIndex, Semantic Kernel, and vector databases.
- Automate ETL/ELT and feature pipelines using Airflow, Prefect, Dagster, dbt, Kafka, or Kinesis.
- Provision cloud and AI infrastructure with Terraform, Pulumi, CloudFormation, or Azure Bicep.
- Implement event-driven architectures, serverless functions, monitoring, logging, GitOps deployments, and progressive delivery.
- Secure cloud environments using IAM, workload identities, least-privilege controls, and AI security practices.
- Implement model and data observability for drift, bias, and performance tracking.
- Develop automation and orchestration scripts using Python, Bash, PowerShell, and SQL.
- Collaborate with data engineers, ML engineers, DevOps teams, and security teams while contributing to documentation, reviews, and junior mentoring.
Requirements
- 4–7 years of hands-on experience in cloud, DevOps, and AI/ML workflows.
- B.Tech. or BS in Computer Science.
- Strong experience with Terraform, Kubernetes, Helm, Docker, and CI/CD tools including GitHub Actions, GitLab CI, Jenkins, and Azure DevOps.
- Proficiency in Python and scripting with Bash or PowerShell.
- Experience with DevSecOps practices including SAST/DAST, container scanning, secrets scanning, SBOM, and policy-as-code.
- Exposure to MLOps and AI integration using MLflow, Kubeflow, SageMaker, Azure ML, KServe, or Seldon.
- Familiarity with AWS, Azure, GCP, configuration management, and GitOps tools such as Argo CD or Flux.
- Strong communication, troubleshooting, collaboration, and cross-functional working skills.
- Experience with Ansible or Puppet, Kubernetes deployment tooling, model-serving frameworks, vector databases, observability platforms, and serverless technologies is desired.
Tech Stack
AnsibleApache AirflowApache KafkaArgo CDAWSAzureBashChefCircleCIDatadogdbtDockerGitHub ActionsGitLab CI/CDGoogle Cloud PlatformGrafanaGraphQLgRPCHelmJenkinsKubernetesMLflowPowerShellPrometheusPuppetPythonRabbitMQSonarQubeSQLTerraform
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