Sr. MLOps Engineer

Required Skills & Experience

• 10+ years of experience in DevSecOps, Platform Engineering, MLOps, or equivalent senior software engineering roles.
• Strong experience with CI/CD tooling (Git, Bitbucket, Jenkins).
• Expertise in Docker and Kubernetes and cloud-native architectures.
• Proficiency in Python, Bash, or PowerShell scripting.
• Hands-on experience with AWS, Azure, or Google Cloud Platform.
• Experience with Infrastructure as Code (Terraform, ARM) and configuration management (Ansible, Puppet, or Chef).
• Familiarity with code quality and security tools such as Veracode and SonarQube.
• Solid understanding of Linux, networking, and storage concepts (block, object, file).
• Knowledge of CNCF ecosystem tools, autoscaling strategies, and cloud observability.
• Working knowledge of SQL fundamentals and messaging systems (Kafka, RabbitMQ).
• Experience implementing monitoring and logging solutions for distributed systems.
• Bachelors degree in Computer Science, Information Technology, or a related field.

Nice to Have Skills & Experience

Masters degree in a related technical field.

Experience with Jira, Confluence, or similar work and knowledge management tools.

Deployment experience with ERP, CRM, WMS, or eCommerce platforms.

Experience supporting LLM-based systems in production environments.

Job Description

As a Senior DevSecOps & MLOps Engineer, you will lead the design, automation, and governance of secure CI/CD and ML pipelines to enable reliable, scalable, and compliant software and AI delivery. You will partner closely with application development, data science, infrastructure, and security teams to build resilient runtime platforms, standardize deployment workflows, and embed security and observability across the full software and machine learning lifecycle.

This role blends DevSecOps, platform engineering, and MLOps, with a strong emphasis on automation, security, and operational excellence.

Key Responsibilities

DevSecOps & Platform Engineering

• Architect, implement, and maintain secure, scalable CI/CD pipelines for application and AI workloads.
• Define and enforce best practices for source control, release management, infrastructure-as-code, and information security.
• Analyze code repositories for security vulnerabilities and compliance issues, driving remediation strategies.
• Deploy, operate, and optimize containerized platforms using Docker and Kubernetes.
• Build and maintain secure, resilient runtime environments across SaaS, IaaS, and PaaS platforms.
• Collaborate with platform leads and third-party vendors to assess, test, and deploy upgrades and patches.
• Resolve complex environmental and deployment issues in partnership with external technical account managers.
• Automate infrastructure provisioning and configuration using Terraform, Ansible, and Azure Resource Manager (ARM).
• Plan and execute production deployments with minimal downtime using proven release strategies.

MLOps & AI Deployment

• Support the full machine learning lifecycle, including model training, validation, deployment, monitoring, and retirement.
• Build and maintain ML pipelines with tools such as MLflow, Kubeflow, or equivalent frameworks.
• Manage model versioning, lifecycle management, and artifact traceability.
• Deploy ML models as production-grade APIs using REST or gRPC.
• Implement model serving solutions using FastAPI, TorchServe, KFServing, or similar frameworks.
• Design and operate batch and real-time inference pipelines.
• Implement canary and blue-green deployment strategies for ML models.
• Standardize AI deployment workflows by building reusable pipeline templates and enabling self-service for development and data science teams.

Observability, Reliability & Security

• Implement observability for applications and AI systems, including logging, monitoring, and alerting.
• Monitor model performance, accuracy, and drift (data drift and concept drift).
• Enable AI observability by logging model inputs and outputs for traceability and auditability.
• Design and integrate feedback loops to continuously improve model performance.
• Secure ML pipelines and model endpoints, including authentication, authorization, and secrets management.
• Ensure proper handling of PII and sensitive data in AI workflows.
• Promote Responsible AI practices, including governance, auditability, and risk mitigation.
• Maintain awareness of prompt injection risks and security concerns for LLM-based systems.

Collaboration & Ways of Working

Champion secure development and operational best practices across engineering and data teams.

Act as a trusted partner and advisor for DevSecOps, MLOps, and platform engineering initiatives.

Actively participate in agile ceremonies, peer reviews, and knowledge-sharing activities.

Produce and maintain high-quality documentation, including architecture diagrams, specifications, runbooks, and procedures.

Provide responsive triage and resolution for issues across development, staging, and production environments.

Foster open, professional communication across all levels of the organization.