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 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 requirementsref : hirist.tech)