Senior Software Engineer ML

It is a global technology company, provides shipping & mailing solutions powering billions of physical and digital transactions in the connected and borderless world of commerce.

The Job
• Understanding of machine learning strategies. You will be working closely with the Data Scientist & SMEs across geographies for data science projects.
• Work with a highly integrated, cross-discipline agile team and will be responsible for Building Designing, Prototyping, and Refining scalable infrastructure for operating Pitney Bowes's machine learning pipeline at scale.
• Strong knowledge on machine learning design system such as inference optimization, model compression, scaling, monitoring, ground truth etc.
• Demonstrated self-motivation and willingness to dive into complicated Full Stack Engineering challenges.
• Work on a backlog of activities to raise MLOps maturity in the organization.
• Deployed applications using cloud platforms or your own premise servers using open source stack.
• Good storyteller for non-technical users such business users, product owners and analyst.
• Take ownership of all assigned tasks and project related assignments.

Required Qualifications & Skills
This role requires a talented self-directed individual with a strong work ethic and the following skills:

Must to have
• Software engineering experience in Python or similar programming languages to contribute to a Python code base.
• Experience architecting, building, and deploying scalable data / Data science applications into AWS cloud using ECS, EKS, Lambda, API Gateway, Sagemaker, AWS Glue, Steps Function, DynamoDB, and S3
• Experience is SQL and Databases such as Amazon RDS, MySql
• Experience on Django/Flask, FastAPIs, Gunicorn, nginx
• Experience working with Docker, Kubernetes, Argo, DVC, CI/CD pipelines using Gitlab CI/CD and familiarity with infrastructure as code principles.
• Hands on experience working on Production Solutions with understanding on Scalability, Reliability, Uptime, Cost Optimization of a solution.

Good to have
• Spark, Kubernetes, Docker, ECS, EKS
• Automation of ETL Jobs and orchestration using scripts, pipelines, workflows.
• Exposure of ML fundamentals and multiple deep-learning frameworks, such as Caffe, TensorFlow, Torch/PyTorch.
Qualification & work experience
• UG - B.Tech/B.E. OR PG – M.S. / M.Tech from REC or any other reputed institute
• 1+ years of Experience on AWS Sagemaker
• 2+ years' experience in an AWS (cloud) development environment with experience working with hands on experience designing and implementing solutions.
• 1+ year experience on Snowflake
• 2+ years in python, Django.
• 2+ year of Industry experience on working as a Machine learning engineer.