Lead cross-functional technical teams across distributed systems, program management, app engineering and product teams while working on all aspects of design, development and delivery of machine learning enabled end-to-end pipelines and solutions.
Design and architect microservices to support the full Machine learning Lifecycle right from Development to Deployment
Build scalable micro services architectures than can handle high volume data requests and high concurrency
Architect solutions for High Availability, Low Latency and High Throughput.
Improve system reliability-Troubleshoot and investigate any identified issues
Build reusable patterns, libraries, and perfect test cases to introduce highly reliable and scalable application platforms
Writing high-performance, reliable, and maintainable code.
Interface with various interactive services and clients including web and mobile applications.
Experience in leading a team as well as handling customer stakeholders
Hands on experience in building scalable solutions using microservices & deployment of ML Algorithms in production
Excellent programming skills in Python, a good understanding in Go is a plus.
Experience in Test driven Development
Expert knowledge of building and maintaining in Production
Experience in cloud - AWS/Azure/Google is mandatory
Exposure to Front end frameworks like React/Angular having led teams and built applications
Experience is creating reusable components and libraries
Experience is optimising the performance of the web applications
Exposure to mobile application development is a plus
Proficient with SQL, RDBMS such as Postgres, MySQL, SQL Server, Oracle and/or experience with NoSQL DBMSs such as Mongo DB
Familiarity with some ORM (Object Relational Mapper) libraries like SQLAlchemy
Experience in optimising the performance of the queries.
Experience in server-side/back-end full cycle product development in a production environment in Python
Hands on experience in python server frameworks like Django/Flask/FastAPI
Strong testing and debugging skills.
Strong trouble-shooting skills that span systems (Linux), network, and application
Understanding of the threading limitations of Python, and multi-process architecture.
Good knowledge of software engineering practices like version control (GIT) and Dev ops
Understanding of ML/AI Pipeline & Development life cycle & tools, MLOps Knowledge
Leveraging Agile and Lean software development methodologies to drive reliability upstream into the product development life cycle.
