The scope of the consultant services is to assist in
As Software Engineer, your main responsibilities will include:
Building software that meets the team s & company s engineering standards.
Contribute to all aspects of the platform lifecycle, with a focus on engineering (including new product ideas)
Lead and promote good software engineering practices within an agile/iterative development approach to improve time to market and fulfill business needs.
Partner closely with the engineering manager. Onboard, coach and mentor engineers to secure competence transfer and a high-performing team.
Design and educate other engineers on ways of working, encouraging good practices to meet consumer expectations on product or service delivery.
Define solution architecture and contribute to the landscape architecture.
Define, maintain, and improve our integration and delivery pipeline.
Explore and apply new technologies suitable for our product.
Take responsibility for the product from ideation to runtime; this may include on-call duties.
Continuously nurture skills in areas such as system architecture, infrastructure management, software development, and platform engineering.
Develop tools, frameworks, and custom components to address common needs in machine learning platforms, such as model training, model deployments, model severability, versioning, explainable, feature store, security, infrastructure, etc.
Design, Develop, and maintain large-scale data and cloud infrastructure required for machine learning projects.
Working with CI/CD flow where we strive for total automation of bringing code from a developer to production.
Utilize software engineering to create efficient, scalable solutions for deployment in critical production environments hosted on GCP/Azure.
Understanding of Large Language Models.
Leverage your expertise in working with GCP and Azure as a foundation for scaling AI and ML solutions.
What 3 things are most important?
Azure Cloud
Experience working with open-source technologies like Nvidia Nemo Framework, Mongo, TensorRT, k Serve, K-Native, Apache Kafka, etc.
Have solid foundations on DevOps principles and possess hands-on experience with modern DevOps practices.