PLEASE NOTE THAT THIS IS A CONTRACT-TO-HIRE POSITION! Candidates who are unable to accept CTH opportunities will NOT be considered.
Seeking a self-starter who can work independently to implement and operate new technologies that help advance Automation & Artificial Intelligence objectives.
Responsibilities to include:
- Build, tune and scale out the ML models as per Business needs. Specialize in NLP/NLU models.
- Perform data analysis and create data visualizations and reports.
- Productionize models developed. Examples include refactoring Python code written on Jupyter Notebook to PySpark.
- Develop AI and ML pipelines for continuous operation, feedback and monitoring of ML models demonstrating standard processes from the CI/CD vertical within the MLOps domain. This can include supervising for data drift, triggering model retraining and setting up rollbacks.
- Optimize AI development environments (development, testing, production) for usability, reliability and performance.
- Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications
- Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines fostering these repositories and the ML feature or data stores are working as intended.
- Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. This also involves staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams.
Technical Skills:
- Be well-versed in software engineering (C++, Java, Python), infrastructure provisioning and DevOps principles, whether they are related to infrastructure as code, microservices architecture or CI/CD automation.
- Ability to evaluate the performance and supervising characteristics of the ML model. These include model size (what is the size of the model), inference performance (speed at which results are returned for inference), memory consumption (how much memory will be consumed once in production), model observability and drift.
- Are you familiar with ML algorithms, AI use cases and applications? Should have knowledge of, but not expertise in, open-source high-code frameworks like PyTorch or TensorFlow, augmented AI and ML platforms, pretrained ML models and integrated AI PaaS tools. Examples include Azure Machine Learning Studio, Google’s Vertex AI, IBM Watson Studio, Amazon SageMaker and open-source tools like Kubeflow.
- Have knowledge about data engineering concepts, tools and automation processes (DataOps) since data pipelines and architectures provide the base for building AI solutions. Examples include MPP data warehouses like Snowflake and Amazon Redshift and all-in-one Apache Spark platforms like Databricks.
Non-Technical Skills:
- AI strategy development! They should help devise, along with data scientists and ML architects, the long-term AI growth plan, keeping in mind the scalability and availability of resources.