MLSecOps Engineer

We are seeking a highly skilled MLSecOps Engineer with 7+ years of experience in Machine Learning operations, security, and DevSecOps practices. This role focuses on ensuring that ML systems, pipelines, and models are secure, compliant, scalable, and robust across their entire lifecycle.
You will collaborate closely with DevOps engineers, platform teams, and security teams to implement secure-by-design AI/ML workflows, defend against emerging ML‑specific attacks, and operationalize best‑in‑class governance.

Dep

Qualitia Software Pvt Ltd

Open Positions

1

Mandatory Skills

Machine Learning,Python,MLOps,DevOps,Ci/Cd

Roles & Responsibilities

ML Security (Core MLSecOps)

• Implement security best practices across ML pipelines, model training, model deployment, and monitoring.
• Secure ML workflows against threats including:
  *Model poisoning
  *Data poisoning
  *Adversarial attacks
  *Model theft / model extraction
  *Prompt injection / prompt hacking (for GenAI models)
• Ensure end-to-end model governance and AI risk management (bias, drift, explainability, lineage).
• Work with teams to implement RBAC, secrets management, encryption, and audit logging.
• Lead threat modeling and risk assessment for machine learning and GenAI systems.

MLOps & Platform Responsibilities

• Build and maintain secure, scalable ML pipelines (CI/CD for ML).
• Work with ML platforms like:
  *Azure ML
  *AWS SageMaker
  *GCP Vertex AI
  *Databricks
• Automate monitoring for:
  *Data drift
  *Model drift
  *Concept drift
  *Performance degradation
• Ensure regulatory compliance (GDPR, SOC2, ISO27001, AI Governance frameworks).

DevSecOps

• Integrate ML workflows into secure DevOps pipelines with automated scanning (SAST/DAST), vulnerability assessment, and artifact security.
• Enforce container security using tools like:
  *Trivy
  *Aqua Security
  *Twistlock
  *Implement policy-as-code (OPA / Kyverno) for deployment governance.

GenAI Security

• Develop and secure workflows for LLM-based solutions (e.g., GPT, Llama, Claude, Azure OpenAI).
• Implement defenses against:
  *Jailbreak attacks
  *Prompt leakage
  *Context/embedding manipulation
  *Unsafe output
• Secure vector databases (Pinecone, Weaviate, FAISS) and embedding services.

CI/CD for ML (CI/CD/CT)

• Build and maintain CI/CD pipelines for ML model development and deployment.
• Automate model training, packaging, artifact versioning, and deployment via:
  *GitLab CI
  *Azure DevOps
  *Jenkins
• Implement continuous training (CT) and continuous monitoring (CM) workflows.
• Infrastructure & Cloud DevOps
• Build secure and scalable ML infrastructure on one for the following: Azure, AWS, GCP
• Use Infrastructure as Code (IaC) tools: Terraform, ARM/Bicep, CloudFormation
• Manage containerized ML applications with: Docker, Kubernetes (AKS/EKS/GKE)
• Security in DevOps (DevSecOps)
  *Integrate SAST, DAST, and dependency scanning into ML pipelines.
  *Enforce container image scanning (Trivy, Aqua, Twistlock).
  *Implement vulnerability remediation workflows and policy-as-code (OPA, Kyverno).
  *Secure artifact repositories and registries.

Collaboration & Documentation

• Conduct ongoing audits and assessments of ML models and pipelines.
• Collaborate with data science teams to embed security in the training-to-deployment lifecycle.
• Document ML security standards, guidelines, and playbooks.

Required Skills & Qualifications

• 7+ years of experience in ML, MLOps, DevSecOps, or security engineering.
• Strong hands-on experience with:
  *Python for ML security tooling and pipeline development.
  *ML frameworks (TensorFlow, PyTorch, Scikit‑Learn).
  *MLOps platforms (Azure ML, SageMaker, Vertex AI, Databricks).
• Deep understanding of ML security risks, adversarial ML, and ML attack surfaces.
• Experience securing CI/CD pipelines for ML (GitHub Actions, GitLab, Azure DevOps, Jenkins).
• Experience with security scanning, vuln management, and policy enforcement.
• Familiarity with deploying models in Kubernetes, Docker, or serverless environments.
• Strong understanding of cloud security practices (AWS/Azure/GCP).
• Experience in monitoring & logging tools (ELK, Prometheus, Grafana).
• Knowledge of AI governance, responsible AI, and model compliance frameworks.
• Strong DevOps experience: Kubernetes + Docker, Terraform / IaC
• Monitoring & logging stacks

Qualification

BE

Years Of Experience

7 to 10 years