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MLOps Architect

MLOps Architect

MINDTEL GLOBAL PRIVATE LIMITEDGurgaon
30+ days ago
Job description

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 requirements
  • ref : hirist.tech)

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