Tech S And T-Network Engineer Senior-GDSN02

Talentmate; Chennai District; On-site; INR 900,000 - 1,400,000; Full time

Job summary

EY in Chennai hires a DevSecOps & AI Engineer to build secure CI/CD pipelines and integrate AI/ML workloads with industry-leading tools. You’ll collaborate across security, data engineering, and DevOps to deliver scalable, reliable systems.

The role emphasizes shift-left security, IaC, MLOps, and MLO tooling with cloud platforms (AWS/Azure/GCP) and Kubernetes, guiding best practices, governance, and observability while mentoring juniors and documenting processes.

Qualifications

• 4–7 years hands-on experience in cloud, DevOps, and AI/ML workflows.
• Strong skills in Terraform, Kubernetes, Helm, Docker, and CI/CD (GitHub Actions, GitLab CI, Jenkins, Azure DevOps).
• Proficient in Python and scripting (Bash/PowerShell) with good automation mindset.
• Experience implementing DevSecOps practices—SAST/DAST, container scanning, secrets scanning, SBOM, and policy-as-code.

Responsibilities

• Build and maintain secure CI/CD pipelines for application, data, and AI workloads.
• Integrate DevSecOps practices into pipelines with security scanning tools.
• Develop and automate MLOps workflows for model training, packaging, and deployment.

Skills

Terraform

Kubernetes

CI/CD

Python scripting

GitOps

DevSecOps practices

MLOps

Cloud platforms

Communication

Education

B.Tech / BS in Computer Science

Tools

Terraform

Kubernetes

Docker/Podman

Helm/Kustomize

Argo CD/Flux

Snyk/SonarQube/Trivy

MLflow/Kubeflow

SageMaker/Azure ML/Vertex AI

Job description

Job Description:

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

DevSecOps & AI Engineer

Job Description

• Build and maintain secure CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, Azure DevOps, and CircleCI for application, data, and AI workloads.
• Integrate DevSecOps practices into pipelines using Snyk, SonarQube, Checkmarx, Trivy, Anchore, and OWASP tools for continuous security scanning.
• Implement shift-left security with secret scanning (GitLeaks, TruffleHog), SBOM automation (Syft, CycloneDX), and dependency management (Dependabot, Renovate).
• Work with containerization (Docker/Podman) and Kubernetes (EKS, AKS, GKE) including Helm/Kustomize for deployments and secure image pipelines.
• Develop and automate MLOps workflows using MLflow, Kubeflow, Azure ML, SageMaker, or Vertex AI for model training, packaging, and deployment.
• Build and maintain RAG/AI integration pipelines using LangChain, LlamaIndex, Semantic Kernel, and vector databases like Pinecone, Weaviate, or FAISS.
• Implement AI inference systems using Seldon Core, KServe, BentoML, Ray Serve, or Triton Inference Server for scalable model serving.
• Automate ETL/ELT and data feature pipelines using Airflow, Prefect, Dagster, dbt, or Kafka/Kinesis for AI model data feeds.
• Work with IaC tools such as Terraform, Pulumi, CloudFormation, or Azure Bicep to provision cloud and AI infrastructure.
• Implement event-driven architectures using serverless functions (AWS Lambda, Azure Functions, Cloud Functions) and messaging systems like Kafka or RabbitMQ.
• Maintain monitoring and logging using Prometheus, Grafana, ELK/Loki, OpenTelemetry, Jaeger, Datadog, or New Relic for both app and ML workloads.
• Handle model & data observability using tools like Evidently AI, Arize AI, WhyLabs, or Fiddler for drift, bias, and performance tracking.
• Secure cloud environments using IAM best practices (AWS IAM, Azure AD/Entra ID, GCP IAM), workload identities, and least-privilege controls.
• Support configuration management using Ansible, Chef, or SaltStack for environment consistency and automation.
• Develop scripts in Python, Bash, or SQL for automation, data processing, validation, and orchestration of ML workflows.
• Implement API integrations for AI systems using REST, gRPC, or GraphQL for model consumption and downstream applications.
• Use GitOps tools like Argo CD or Flux for automated, secure Kubernetes deployments and progressive delivery.
• Apply AI security practices including guardrails, prompt protection, model validation, and safe inference techniques using industry tools.
• Ensure compliance with data governance, privacy, and security standards including GDPR, CCPA, and cloud security best practices.
• Collaborate with data engineers, ML engineers, DevOps teams, and security teams, contributing to documentation, reviews, and mentoring juniors.

Desired Profile

• Looking for a DevSecOps & AI Engineer with 4–7 years of hands‑on experience in cloud, DevOps, and AI/ML workflows.
• Strong skills in Terraform, Kubernetes, Helm, Docker, and CI/CD (GitHub Actions, GitLab CI, Jenkins, Azure DevOps).
• Proficient in Python and scripting (Bash/PowerShell) with good automation mindset.
• Experience implementing DevSecOps practices—SAST/DAST, container scanning, secrets scanning, SBOM, and policy-as-code.
• Exposure to MLOps/AI integration using MLflow, Kubeflow, SageMaker, Azure ML, KServe, or Seldon.
• Familiar with cloud (AWS/Azure/GCP), configuration management (Ansible/Puppet), and GitOps tools (Argo CD/Flux).
• Strong communication, troubleshooting, and collaboration skills with ability to work cross‑functionally.

Experience

• 4 to 7 years

Education

• B.Tech. / BS in Computer Science

Technical Skills & Certifications

• Terraform, Pulumi, and Infrastructure as Code (IaC)
• Kubernetes (EKS/AKS/GKE), Docker/Podman, Helm, Kustomize
• CI/CD tools: GitHub Actions, GitLab CI, Jenkins, Azure DevOps
• Cloud platforms: AWS, Azure, GCP
• Python, Bash, PowerShell scripting
• DevSecOps tools: Snyk, SonarQube, Trivy, Checkmarx, GitLeaks, TruffleHog
• Policy-as-code (OPA/Gatekeeper, Kyverno) and SBOM tools (Syft, CycloneDX)
• MLOps tools: MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI
• Model serving frameworks: KServe, Seldon Core, BentoML, Ray Serve
• Vector DBs & RAG stack: Pinecone, Weaviate, FAISS, Chroma, LangChain, LlamaIndex
• Monitoring & observability: Prometheus, Grafana, ELK/Loki, OpenTelemetry, Jaeger
• Configuration management: Ansible, Puppet
• GitOps: Argo CD, Flux
• Serverless: AWS Lambda, Azure Functions, Google Cloud Functions.

EY | Building a better working world

EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.

Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.

Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

Requirements: