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Designation 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 Current Relic for both app and ML workloads.
Handle model & dat

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