A bit about Whispir:
Whispir is a cloud software and communications platform that seeks to help organisations communicate like people. Our platform enables our customers to build powerful communications workflows and automate interactions between systems, business, devices and people - with no coding required.
The outcomes are powerful – people everywhere can have access to the right information at the right time so that they can make informed decisions, take action, engage and communicate with each other.
Organisations can finally start to behave how they have always wanted to – they can engage with people the way people do... in a timely, personal and contextually sensitive way with actionable, useful insights that promote richer and deeper engagement.
We know this because leading organisations use Whispir's technology locally and globally every day for this reason. Whispir becomes their competitive advantage as they streamline their operations, improve their resilience and bring innovative new services to market quickly. We also see first-hand the capacity of these innovative solutions to assist saving lives and materially improve community outcomes.
About the position:
You will be the bridge between Data Science and Software Engineering. You will be working closely with Data Scientists to bring ML powered solutions to production, facing both internally and externally.
Our Tech Stack / Tools
- Programming languages we use Python, Bash
- ML Libraries such as Tensorflow, Scikit Learn, HuggingFace (transformers, datasets)
- Data Processing libraries such as Numpy, Pandas, Dask
- Configuration languages Yaml, Terraform Configuration Language
- Our applications are currently deployed to AWS using Sagemaker, Lambdas, API Gateway etc.
- Our ML platform will have Kubernetes and Kubeflow at its heart
- Support Data Scientists with ML Development by both working with them hand in hand and building tools/platforms to support them
- Architect, Build, Deploy and Maintain ML powered solutions (typically internal APIs, could also be stand alone microservices/applications)
- Engage stakeholders, work with other teams and collaborate with internal and external customers
- Independent Problem Solver, ability to own and drive projects (especially in the face of uncertainty)
- Continuous Learning, doesn’t hesitate to learn new skills on the fly
- Appreciates the importance of collaboration, both intra-team and inter-team
- Enjoys sussing out new tools
- Passionate about Machine Learning and Software Engineering
- 2+ Years of Experience as an ML Engineer / Software Engineer (Machine Learning) / MLOps Engineer
- Software Engineering skills to write production grade code
- Foundational Software Engineering skills around writing clean code, familiarity with SOLID/DRY principles, SDLC, TDD, general ability to maintain a codebase in a good shape
- Solid programming skills in at least one Programming language Python, Scala, Go etc.
- ML Engineering skills to bridge the gap between Data Science and Software Engineering worlds by working closely with them to package models into applications
- Familiarity (and preferably hands-on working experience) with key Machine Learning and Data Science fundamentals
- Excellent understanding of & experience (to varying degrees) in end-to-end Machine Learning development lifecycle all the way from ideation/brainstorming to proof of concepts/prototyping, data ingestion, exploration, model building to productionising, deploying, monitoring and maintaining ML Solutions/Applications
- MLOps/DevOps Platform Experience to Architect, Build and Maintain Infrastructure for ML
- Experience with CI/CD pipelines, Automated testing/Continuous deployments, GitOps experience would be a bonus
- Experience with Containerisation using Docker and Kubernetes
- Familiarity/Experience with Microservices and Event Driven Architecture patterns
- Experience with Architecting, standing up and maintaining infrastructure (preferably with infrastructure as code) on one of the major cloud providers
- DevOps / MLOps experience specifically in the areas of Distributed training, Data Versioning/Lineage, ML Pipelines, API design etc.
- With focus on First Principles around Clean Design, Scalability, Security and Reliability
Please note that this position description is not intended to constitute a complete list of your duties. You may be required to carry out other duties consistent with the position. Further, the Company may also, from time to time and at its discretion, change your duties in any reasonable manner after consultation with you.