MLOps Architect

We are seeking a visionary MLOps Architect to lead the design and implementation of scalable, secure, and high-performance MLOps frameworks across enterprise AI/ML initiatives.

This role is ideal for a seasoned technology leader with deep expertise in machine learning pipelines, DevOps best practices, and model lifecycle management using modern tools like MLflow, Feast, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).

Key Responsibilities :

- Design and implement end-to-end MLOps architectures supporting robust and repeatable machine learning workflows.

- Build and maintain ML pipelines covering data ingestion, feature engineering, training, validation, deployment, and monitoring.

- Implement and manage feature stores using Feast, ensuring reuse, versioning, and governance of features across teams.

- Use MLflow for experiment tracking, model registry, and model packaging, enforcing standardization and reproducibility.

- Architect and deploy scalable model serving infrastructure (batch, streaming, real-time) using Docker, Kubernetes, and related tools.

- Set up comprehensive model monitoring systems for performance metrics, drift detection, and alerting to ensure model reliability in production.

- Champion CI/CD practices tailored for ML workflows using tools such as Jenkins, GitLab CI, or Azure DevOps.

- Apply Infrastructure as Code (IaC) principles using Terraform, CloudFormation, or similar tools for scalable, repeatable deployment.

- Collaborate with data scientists, engineers, and platform teams to streamline ML development, testing, and operationalization.

- Provide architectural guidance, code reviews, and best practices to cross-functional teams.

Required Qualifications & Skills :

- 14+ years in software engineering, data engineering, or data science roles.

- 45 years in designing and operationalizing MLOps platforms.

- Hands-on experience with tracking experiments, model packaging, and managing model registries.

- Proven experience setting up and scaling feature stores in production.

- Expertise in deployment patterns (batch, real-time, A/B, canary) using Docker, Kubernetes, Flask/FastAPI, etc.

- Strong knowledge of building monitoring solutions with logging, alerting, performance tracking, and drift detection.

- Extensive experience in scripting, automation, data manipulation, and API development.

- Solid experience with at least one major cloud provider AWS, Azure, or GCP.

- Familiarity with Jenkins, GitLab CI, ArgoCD, and Terraform/CloudFormation.

Preferred Qualifications :

- Experience integrating MLOps solutions with data platforms like Databricks, Apache Spark, or Snowflake.

- Understanding of security best practices in ML systems including model encryption, access control, and auditing.

- Certification in cloud platforms (AWS/GCP/Azure) or MLOps tools is a plus.

- Experience working in regulated industries (e., finance, healthcare) with strict compliance requirements