ML & MLOps Lead Engineer

You are as unique as your background, experience and point of view. Here, you’ll be encouraged, empowered and challenged to be your best self. You'll work with dynamic colleagues - experts in their fields - who are eager to share their knowledge with you. Your leaders will inspire and help you reach your potential and soar to new heights. Every day, you'll have new and exciting opportunities to make life brighter for our Clients - who are at the heart of everything we do. Discover how you can make a difference in the lives of individuals, families and communities around the world.

Job Description:

BASIC QUALIFICATIONS

• Bachelor's or master’s degree in computer science, IT or related technical field

• Minimum 11-15 years of professional software development experience
• Strong communication skills with ability to work with business and Technology stakeholders
• Minimum 5 years of experience with designing robust solutions on Cloud (preferably AWS)
• Hands on experience with programming languages like Python and ML Frameworks like PySpark, PyTorch, TensorFlow
• Strong experience with AWS data services like SageMaker, Glue, Athena, Bedrock, Lake Formation, S3, RDS, ECR, EMR etc.

PREFERRED QUALIFICATIONS

• Proficiency in designing the solutions in building Data & ML Pipelines covering all stages of ML model deployment and its monitoring
• Experience working in Insurance / Finance domain
• Strong knowledge of working in containerization environment e.g. Docker
• Experience with Generative AI models and techniques
• Strong understanding of data engineering concepts, including data warehousing, ETL processes, and data pipeline development
• Review and attest the solutions implementation as per approved architecture / guidelines
• Ability to design and represent solution architecture / Integration Pattern at various Architecture Forums for approval
• Collaborate with Regional and Market Architecture Teams
• Enforce security standards as per organization security directives on the solutions.
• Experience in building reusable components for data scientists and assisting them in all stages of Model development and deployment. Must have experience in AWS SageMaker and AWS SageMaker Studio
• Experience with IDE/notebook software (Jupyter Studio,, VSCode, PyCharm, etc)
• Experience in building data pipeline using on cloud using Cloud technologies (S3, Lakeformation, SQS, SNS, Spark, Glue, Step Functions, Lambda etc)
• Good to have experience in Data Visualization tools like Tableau, AWS Quicksight, PowerBI

Key Responsibilities:

• Cloud Solution Design and Implementation: Develop and deploy machine learning operations and solutions on cloud platforms, with a preference for AWS.
• Architecture Governance: Engage in the architecture governance process to secure approvals for proposed solution designs
• Stakeholder Management: Exhibit strong written and verbal technical communication skills, presenting complex technical information clearly and concisely to diverse audiences.
• Model and Data Pipeline Development: Construct and maintain model and data pipelines to support Data Scientists and Data Engineers
• Cloud Environment Setup: Configure cloud environments and manage Docker images both on-premise and in the cloud
• Model Review and Optimization: Support data science model reviews, including code refactoring, optimization, containerization, deployment, versioning, and quality monitoring.
• AWS SageMaker Expertise: Utilize various AWS SageMaker features such as SageMaker Studio, Jupyter Notebooks, Data Wrangler, and Clarify
• Python ML Libraries: Demonstrate proficiency with Python ML libraries, including the ability to prototype and evaluate new libraries and features
• Cross-Team Communication: Collaborate with data science, cloud infrastructure, and development teams to gather requirements and explain software product features and technical designs
• Adaptability and Learning: Show capability and eagerness to handle multiple tasks and quickly learn new technologies

Job Category:

Posting End Date: