Note: In-Person Interview (must)

Senior MLOps Engineer

Introduction

This Senior MLOps Engineer position is a critical role within our team, responsible for managing and optimizing the machine learning operations lifecycle. The ideal candidate will have extensive experience in Python, Kubernetes, Continuous Integration, Cloud Computing, and other related technologies. As a part of our team, you will play a key role in deploying, monitoring, and maintaining machine learning models to drive business insights and decision-making.

Responsibilities

• Deploy and manage machine learning models using AWS SageMaker
• Implement and optimize ML lifecycle management processes
• Develop CI/CD pipelines and automation for model deployment
• Utilize feature stores and model registries for efficient model management
• Monitor and govern machine learning models for performance and compliance
• Design and manage AWS cloud infrastructure using Terraform
• Implement containerization with Docker and orchestration with Kubernetes
• Conduct batch and real-time ML inference for timely decision-making
• Work with Kafka, Spark, and other data engineering tools for data processing
• Leverage MLflow, SageMaker Pipelines, and other tools for model observability
• Collaborate with cross-functional teams to support financial services and banking use cases

Requirements

Required Skills:

• Python proficiency
• Experience in Financial Services or Banking industry
• Knowledge of Kubernetes and Lifecycle Management
• Expertise in Continuous Integration and Continuous Delivery
• Strong background in Data Engineering and Cloud Computing
• Experience with Apache Kafka, Apache Spark, Docker, and Terraform
• Understanding of real-time data processing and SQL
• Experience with Machine Learning Operations (ML Ops)

Preferred Skills:

• Experience with Amazon SageMaker and Amazon Web Services
• Prior experience in the banking sector
• Familiarity with Machine Learning concepts and technologies