MLOPS Architect Opening(12+Years and above )
Role Offshore technical lead for MLOPs team
Technical Lead to design, build, and operate cloud-native solutions on Microsoft Azure, leading end-to-end delivery across MLOps capabilities, drive architecture, engineering excellence, CI/CD automation, and production readiness for applications and ML-enabled services in an enterprise environment, while mentoring engineers and partnering with Product, Data, and DevOps teams.
Key Responsibilities
Architecture & Technical Leadership
- Own the end-to-end technical solution, from requirements through design, implementation, deployment, and production support.
- Lead architecture/design reviews, define patterns for scalability, resilience, and maintainability, and provide technical guidance across squads.
- Translate business requirements into solution designs and run technical workshops with stakeholders.
Azure Engineering
- Lead development of (APIs, microservices, web apps), ensuring clean architecture, secure coding, and testability.
- Build/modernize solutions on Azure (e.g., App Services/Web Apps) and integrate platform services for identity, secrets, monitoring, and messaging.
- Implement design patterns for high availability and disaster recovery, and guide performance tuning and production hardening.
MLOps Delivery & Operations
- Implement and operationalize MLOps pipelines covering: data ingestion training model packaging deployment monitoring retraining.
- Validate and integrate ML workflow components on Azure platforms including Azure Databricks and Azure Data Factory (ADF) where applicable.
- Establish model governance practices (versioning, reproducibility, approvals), and support monitoring practices such as drift/quality signals.
DevOps / CI-CD / Automation
- Build CI/CD pipelines and automate deployments; improve release reliability with quality gates, approvals, and rollback strategies.
- Drive containerization and orchestration adoption (e.g., Docker/Kubernetes) when suitable for workloads.
- Define/automate operational procedures for deployment, configuration, patching, and upgrades.
Quality, Observability & Collaboration
- Ensure test strategy coverage (unit/integration/e2e), and partner with QA for end-to-end validation across data/ML pipelines and web applications.
- Implement observability practices (monitoring, alerts, dashboards), and lead incident triage and root cause analysis for production issues.
- Mentor and coach engineers; promote knowledge sharing and engineering best practices.
Required Qualifications
- 15+ years software engineering experience with 5+ years in a technical leadership role.
- Strong experience designing and deploying solutions on Microsoft Azure (cloud-native services, security, monitoring).
- Experience with Azure Databricks, ADF, and cloud web applications is highly valued.
- Solid CI/CD and automation experience; familiarity with Azure DevOps/Jenkins-style pipelines and Git workflows.
- Containers and orchestration exposure (Docker, Kubernetes).
Understanding of ML lifecycle and MLOps fundamentals; ability to partner effectively with Data Science and Data Engineering teams.
