Technical Lead - Machine Learning & MLOps

Role & 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-premises 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.

Preferred candidate profile

• 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
• Ability to liaise with internal and external stakeholders and adapt approach when dealing with
  technical and non-technical audiences.

Education & Experience

• Bachelor's or masters degree in computer science, IT or related technical field
• Minimum 11-16 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.