DevSecOps Engineer

Description : We are hiring a Senior DevSecOps / Security Engineer with 8+ years of experience securing AWS cloud, on-prem infrastructure, DevOps platforms, MLOps environments, CI/CD pipelines, container orchestration, and data/ML platforms. This role is responsible for creating and maintaining a unified security posture across all systems used by DevOps and MLOps teams including AWS, Kubernetes, EMR, MWAA, Spark, Docker, GitOps, observability tools, and network infrastructure.

Key Responsibilities :

1. Cloud Security (AWS)-

- Secure all AWS resources consumed by DevOps/MLOps/Data Science : EC2, EKS, ECS, EMR, MWAA, S3, RDS, Redshift, Lambda, CloudFront, Glue, Athena, Kinesis, Transit Gateway, VPC Peering.

- Implement IAM least privilege, SCPs, KMS, Secrets Manager, SSO & identity governance.

- Configure AWS-native security : WAF, Shield, GuardDuty, Inspector, Macie, CloudTrail, Config, Security Hub.

- Harden VPC architecture, subnets, routing, SG/NACLs, multi-account environments.

- Ensure encryption of data at rest/in transit across all cloud services.

2. DevOps Security (IaC, CI/CD, Kubernetes, Linux)-

Infrastructure as Code & Automation Security :

- Secure Terraform, CloudFormation, Ansible with policy-as-code (OPA, Checkov, tfsec).

- Enforce misconfiguration scanning and automated remediation.

CI/CD Security :

- Secure Jenkins, GitHub, GitLab pipelines with SAST, DAST, SCA, secrets scanning, image scanning.

- Implement secure build, artifact signing, and deployment workflows.

Containers & Kubernetes :

- Harden Docker images, private registries, runtime policies.

- Enforce EKS security : RBAC, IRSA, PSP/PSS, network policies, runtime monitoring.

- Apply CIS Benchmarks for Kubernetes and Linux.

Monitoring & Reliability :

- Secure observability stack : Grafana, CloudWatch, logging, alerting, anomaly detection.

- Ensure audit logging across cloud/platform layers.

3. MLOps Security (Airflow, EMR, Spark, Data Platforms, ML Pipelines)-

Pipeline & Workflow Security :

- Secure Airflow/MWAA connections, secrets, DAGs, execution environments.

- Harden EMR, Spark jobs, Glue jobs, IAM roles, S3 buckets, encryption, and access policies.

ML Platform Security :

- Secure Jupyter/JupyterHub environments, containerized ML workspaces, and experiment tracking systems.

- Control model access, artifact protection, model registry security, and ML metadata integrity.

Data Security :

- Secure ETL/ML data flows across S3, Redshift, RDS, Glue, Kinesis.

- Enforce data versioning security, lineage tracking, PII protection, and access governance.

ML Observability :

- Implement drift detection (data drift/model drift), feature monitoring, audit logging.

- Integrate ML monitoring with Grafana/Prometheus/CloudWatch.

4. Network & Endpoint Security-

- Manage firewall policies, VPN, IDS/IPS, endpoint protection, secure LAN/WAN, Zero Trust principles.

- Conduct vulnerability assessments, penetration test coordination, and network segmentation.

- Secure remote workforce connectivity and internal office networks.

5. Threat Detection, Incident Response & Compliance-

- Centralize log management (CloudWatch, OpenSearch/ELK, SIEM).

- Build security alerts, automated threat detection, and incident workflows.

- Lead incident containment, forensics, RCA, and remediation.

- Ensure compliance with ISO 27001, SOC 2, GDPR, HIPAA (as applicable).

- Maintain security policies, procedures, RRPs (Runbooks), and audits.