Responsibilities:

• Take offline models data scientists build and turn them into a real machine learning production system.
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients? machine learning systems.
• Support model development, with an emphasis on auditability, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept machine learning systems.
• Collaborate with the data engineers and data scientists on feature development in order to containerise and build out the deployment pipelines for new modules.
• Design, build as well as optimise applications containerisation and orchestration with Docker and Kubernetes and cloud platforms like AWS or Azure.
• Automate applications and infrastructure deployments.
• Maintain knowledge of advances of AI and scalable computing in industry and academia.

Educational Qualifications:

• Tech /M.Tech in Computer Science (preferably with specialization in Artificial Intelligence or Data Science) from a reputed institute. Preferably from IIT/NIT.
• Work experience from a reputed MNC company
• 3-5 years of professional experience working in IT industry with relevant experience

Overview:
The successful candidate will contribute to AI engineering activities focusing on streamlining the process of deploying the ML/DL models to production, and then maintaining and monitoring them. Skills Required:

Essential

• 3-5 years of professional experience working in IT industry with relevant experience

• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
• Strong software engineering skills in complex, multi-language systems
• Fluency in Python or any other related computer programming languages
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs
• Experience developing with containers and Kubernetes in cloud computing environments
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration

Desirable

• Good to have basic Linux shell scripting skills
• Good to have experience in developing and maintaining ML systems built with open-source tools
• Good to have exposure to machine learning methodology and best practices
• Good to have exposure to deep learning approaches and modelling frameworks (PyTorch, Tensorflow, Keras, etc.