Mlops Engineer

Job Description

As a Machine Learning Engineer, you’ll have the opportunity to work alongside industry experts researching, developing, and applying cutting-edge machine learning and artificial intelligence algorithms to build innovative technologies, services, and products that solve the company's hardest problems and accelerate Customer’s core growth.

In this role you will combine your software engineering skills and interest in ML and AI to productionize ML models on GCP. You will build reusable tools and components to enable codeless deployments of ML modeling pipelines. Your work will unleash the next generation of customer experiences and transform the way that we operate day-to-day.  

We're seeking a talented Machine Learning Engineer to join our team and you'll be responsible for developing and deploying ML models that personalize product recommendations and deliver the right products at the right time.

What you'll do:

• Deploy ML models to GCP: Design and implement batch pipelines and real-time microservices for millions of ecommerce customers.
• Personalize the shopping experience: Utilize cutting-edge models to predict customer preferences and recommend relevant products.
• Collaborate with cross-functional teams: Partner with product and engineering teams to integrate ML solutions seamlessly.
• Optimize performance: Leverage best practices like multi-region deployment, traffic splitting, and autoscaling to ensure the efficiency of online model endpoints in Seldon.
• Monitor and debug models in production: Identify and resolve issues to maintain the quality of our ML systems.

What you'll bring:

• 4-6 years of experience in Machine Learning Engineering.
• Must have Strong Python skill set with adequate SQL knowledge.
• Deep understanding of the Model Deployment Lifecycle, including training and serving pipelines.
• Familiarity with ML workflows such as Kubeflow, Airflow, MLFlow, or Argo.
• Experience with model monitoring, debugging, latency optimization, and online inference integration.

Bonus points for exposure to PyTorch, TensorFlow, GCP/AWS, and recommendation/ranking models.