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 fieldMinimum 11-15 years of professional software development experienceStrong communication skills with ability to work with business and Technology stakeholdersMinimum 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, TensorFlowStrong 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 monitoringExperience working in Insurance / Finance domainStrong knowledge of working in containerization environment e.g. DockerExperience with Generative AI models and techniquesStrong understanding of data engineering concepts, including data warehousing, ETL processes, and data pipeline developmentReview and attest the solutions implementation as per approved architecture / guidelinesAbility to design and represent solution architecture / Integration Pattern at various Architecture Forums for approvalCollaborate with Regional and Market Architecture TeamsEnforce 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 StudioExperience 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 designsStakeholder 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 EngineersCloud Environment Setup: Configure cloud environments and manage Docker images both on-premise and in the cloudModel 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 ClarifyPython ML Libraries: Demonstrate proficiency with Python ML libraries, including the ability to prototype and evaluate new libraries and featuresCross-Team Communication: Collaborate with data science, cloud infrastructure, and development teams to gather requirements and explain software product features and technical designsAdaptability and Learning: Show capability and eagerness to handle multiple tasks and quickly learn new technologies

Job Category:

IT - Digital Development

Posting End Date:

30/01/2025