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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 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.

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