MLOps/Senior MLOps Pipeline Architect

1 month ago

Bengaluru, Karnataka, India Straive Full time

About Straive:

Straive is a leading Content and Data Technology company providing data services, subject matter expertise, and technology solutions to various domains.

Data Analytics & AI Solutions, Data AI Powered Operations, and Education & Learning form the core pillars of the company's long-term vision.

The company is a specialized solutions provider to business information providers in finance, insurance, legal, real estate, life sciences, and logistics.

Data Analytics & AI Services: Our Data Solutions business has become critical to our clients' success. We use technology and AI with human experts in loop to create data assets that our clients use to power their data products and their end customers' workflows.

We strive to be our clients' future-fit Analytics and AI partner, helping them build data analytics and AI enterprise capabilities.

Our Global Presence:

We have a multi-geographical resource pool strategically located in eight countries - India, Philippines, USA, Nicaragua, Vietnam, United Kingdom, and Singapore.

Job Summary:

We are looking for an experienced Senior ML Ops Engineer with 3-7 years of experience in the Consumer Packaged Goods (CPG) sector, specializing in Machine Learning.

In this role, you will be responsible for designing, building, and optimizing SRM-specific machine learning pipelines to drive business impact.

Key Responsibilities:

  • Collaboration: Work closely with data scientists and ML engineers throughout the ML lifecycle, supporting model development, deployment, and monitoring.
  • ML Ops Pipeline Development: Design, implement, and optimize ML Ops pipelines using tools and frameworks such as TensorFlow Serving, Kubeflow, MLflow, or similar technologies.
  • Data Pipeline Engineering: Build and maintain data pipelines and infrastructure necessary for enterprise-scale machine learning applications.
  • Cloud Implementation: Develop cloud-based ML Ops solutions on platforms such as Azure ML, Azure SageMaker, or Google Cloud AI Platform.
  • Azure Proficiency: Demonstrated experience with Azure cloud services, including Azure ML, Azure DevOps, AKS, Azure Container Registry, and Azure Log Analytics.
  • Containerization Skills: Familiarity with containerization technologies like Docker and Kubernetes.
  • Model Deployment and Monitoring: Deploy and monitor various machine learning models in production, including text/NLP and generative AI models.
  • CI/CD Automation: Build and maintain CI/CD pipelines using tools such as GitLab CI, GitHub Actions, or Airflow.
  • Model Review and Quality Assurance: Participate in data science model reviews, focusing on code optimization, containerization, deployment, versioning, and quality monitoring.
  • Support Data Model Development: Contribute to data model development with an emphasis on auditability, versioning, and data security.
  • Mentorship: Provide guidance and support to junior engineers, fostering a collaborative and innovative team environment.
  • Qualifications:
  • Experience: Minimum of 3-7 years of relevant work experience in ML Ops.
  • Domain Knowledge: Strong expertise in Generative AI, advanced NLP, and machine learning techniques.
  • Production Experience: Proven experience in deploying and maintaining production-grade AI solutions.
  • Communication Skills: Excellent communication and teamwork skills, with the ability to work independently when needed.
  • Problem-Solving: Strong analytical and problem-solving capabilities.
  • Continuous Learning: Stay informed about the latest advancements in ML Ops technologies and actively explore new tools and techniques.

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