Devsecops And AI Engineer
ey
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.