Lead Mlops Engineer

**About the project**:
We are actively seeking a Lead MLOps Engineer with a strong emphasis on the end-to-end implementation of MLOps solutions, particularly geared towards optimizing customer lifetime value (CLV) in the Retail and E-commerce sectors. In this pivotal role, we require an expert who can drive the development and deployment of a comprehensive MLOps Solution Accelerator. This accelerator will be instrumental in enhancing CLV strategies, ensuring seamless data governance, optimizing data transformation, and establishing robust data lakes and feature stores. Additionally, the Lead MLOps Engineer will collaborate closely with cross-functional teams, including data scientists and software engineers, to seamlessly integrate machine learning models into production systems, all while upholding stringent quality assurance standards.

**Duration**: 6+ months

**Stage**: solution development from scratch

**Areas of Responsibility**:

- ** MLOps Solution Accelerator Development**: The lead engineer will spearhead the development of an MLOps Solution Accelerator tailored to Retail/E-commerce needs. This task involves defining the architecture, selecting appropriate technologies, and overseeing the hands-on implementation of key solution components.
- ** CLV Optimization Strategy Implementation**: The engineer will work closely with data scientists and software engineers to implement data-driven customer lifetime value (CLV) optimization strategies. This includes integrating machine learning models into production systems to enhance CLV for Retail/E-commerce clients.
- ** AWS Expertise and Infrastructure Management**: Given the emphasis on AWS services, the lead engineer will be responsible for architecting and managing scalable ML infrastructure using AWS services such as SageMaker, Glue, Lambda, and more. Infrastructure provisioning using AWS CDK and infrastructure as code (IaC) principles will be a critical task.
- ** ML Model Governance Framework**: Developing and implementing a comprehensive ML model governance framework is a key challenge. This involves establishing robust practices to ensure compliance and governance throughout the ML operations lifecycle.
- ** Quality Assurance and Collaboration**: The lead engineer will define and uphold stringent quality assurance standards for ML systems. Collaboration with cross-functional teams, including mentoring data science and engineering teams, is vital to seamlessly integrate ML models into production systems while maintaining quality and efficiency.

**Skills**:

- ** Machine Learning and Deep Learning Expertise**: Proficiency in a wide range of machine learning and deep learning frameworks and tools is crucial for developing and implementing ML solutions. This includes TensorFlow, PyTorch, scikit-learn, and more.
- ** AWS Proficiency**: A strong command of AWS services and tools, particularly those relevant to ML, is required. This includes expertise in SageMaker, Glue, Lambda, and infrastructure provisioning using AWS CDK and infrastructure as code (IaC) principles.
- ** Data Pipeline Management**: In-depth knowledge and experience in designing and managing data pipelines for both batch and streaming processing is essential for effective data handling and preprocessing.
- ** Containerization**: Proficiency in containerization technologies, especially Docker, is necessary for deploying ML models efficiently and consistently.
- ** CI/CD and DevOps Practices**: Familiarity with continuous integration and continuous deployment (CI/CD) pipelines, as well as infrastructure as code (IaC) principles, is critical for maintaining efficient and scalable ML operations.
- ** ML Model Governance**: The ability to develop and implement robust ML model governance practices and frameworks is essential for ensuring compliance and data security.

**Knowledge**:

- ** Retail/E-commerce Domain Knowledge**: A deep understanding of the Retail and E-commerce industry, including industry-specific challenges, trends, and customer behavior, is essential for tailoring MLOps solutions effectively.
- ** MLOps Best Practices**: In-depth knowledge of MLOps best practices, including the latest methodologies and tools for streamlining the machine learning lifecycle, is required for developing and optimizing the MLOps Solution Accelerator.
- ** AWS Services and Solutions**: Extensive knowledge of AWS services and solutions relevant to machine learning, data engineering, and infrastructure management is crucial for architecting and maintaining scalable ML infrastructure on the cloud platform.
- ** Machine Learning and Deep Learning**: A strong foundation in machine learning and deep learning principles, along with knowledge of a variety of ML/DL frameworks and tools, is necessary for integrating and optimizing machine learning models within Retail/E-commerce environments.
- ** Compliance and Governance**: Knowledge of compliance regulations and governance standards, particularly as the