Staff ML Ops Engineer

3 weeks ago

Allahabad, Uttar Pradesh, India Aviso AI Full time

About Aviso

At Aviso, we're transforming revenue intelligence through advanced machine learning and AI. Our platform helps sales organizations optimize their revenue operations with accurate forecasting, insightful deal analytics, and data-driven recommendations.

We're looking for experienced MLOps Engineers to help scale our AWS-based machine learning infrastructure and operations.

Position Overview

As a Staff MLOps Engineer on the Revenue Intelligence team, you'll lead the design and implementation of our ML production infrastructure on AWS. You'll work closely with data scientists and software engineers to build robust pipelines for model training, deployment, and monitoring using Kubeflow within our AWS environment. Your expertise will directly impact the scalability, reliability, and performance of our ML systems.

Key Responsibilities

  • Design, build, and maintain scalable ML infrastructure on AWS for model training, deployment, and monitoring
  • Lead the development of CI/CD pipelines for ML workflows using Kubeflow and AWS services
  • Implement robust monitoring, logging, and alerting for production ML systems
  • Architect and implement data pipelines that efficiently handle large-scale data processing
  • Collaborate with data scientists to optimize model training and inference workflows
  • Define and implement best practices for model versioning, A/B testing, and gradual deployments
  • Drive infrastructure automation initiatives to improve operational efficiency
  • Mentor junior team members on MLOps best practices and AWS architecture

Requirements

  • 8+ years of experience in software engineering or DevOps roles
  • 5+ years of hands-on experience with AWS services (EKS, ECR, S3, Lambda, Step Functions, etc.)
  • Strong experience with Kubernetes and container orchestration, particularly Kubeflow
  • Proficiency in infrastructure-as-code using Terraform, CloudFormation, or equivalent
  • Experience implementing and operating CI/CD pipelines for ML workloads
  • Strong programming skills in Python and shell scripting
  • Experience with monitoring tools and observability practices for production systems
  • Understanding of ML lifecycle and familiarity with common ML frameworks
  • Experience with distributed systems and data processing at scale
  • Strong communication skills and ability to collaborate effectively with cross-functional teams

Preferred Qualifications

  • Experience with revenue forecasting, classification, or clustering models in production
  • AWS Professional certifications (DevOps, Solutions Architect, etc.)
  • Experience with stream processing frameworks (Kafka, Kinesis)
  • Familiarity with feature stores and ML metadata tracking
  • Experience mentoring junior engineers and technical leadership

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