Tech S And T - Network Engineer Senior - GDSN02

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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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