The AI&Automation team is responsible for coordinating all efforts implied to increase AI&Automation adoption across HP’s Commercial Organization, providing RPA / AI / ML solutions to business use cases.
You will work closely with cross-functional teams to identify opportunities, match with both traditional ML methods (forecasting algorithms, recommender systems) and Generative AI and large language models, and deploy solutions in real-world applications. You’ll be able to develop expertise in AI algorithms, deep learning architectures, and advanced ML techniques to drive innovative AI initiatives within the organization.
Main Responsibilities
* Increase the adoption of AI / ML technology within HP’s Commercial Organization by finding applicability of technology in real business challenges, and ensuring adoption of the solutions built.
* Collaborate with AI& Automation Product Owner to validate backlog priorities and whether similar solutions might be in place and could be scaled / leveraged.
* Engage with business stakeholders who need AI / ML solutions to understand the requirements and recommend the proper solution for their need, connect model results to business strategy, and produce recommendations that match business situations.
* Collaborate with cross-functional teams : data scientists, data / cloud engineers, fe / be / RPA developers, and domain experts to ensure the business requirements for the identified AI opportunities are properly turned into viable and sustainable AI solutions that address specific challenges and can be adopted.
* Design and implement models to discover patterns and predictions that generate business value and innovation. Develop and implement AI / ML models : from data preprocessing and feature engineering to ensure high-quality data inputs for AI models to model development and fine-tuning to address issues of overfitting or underfitting. Deliver performance evaluations and model validations to assess the effectiveness of the AI / ML models in our portfolio.
* Communicate insights and recommendations : Present findings, insights, and recommendations to stakeholders in a clear and concise manner, adapted to business understanding. Translate complex technical concepts into actionable insights for non-technical audiences.
* Stay updated with AI research and advancements : Keep abreast of the latest trends, techniques, and research papers in the field of AI and machine learning. Continuously explore new algorithms and approaches to enhance the organization's AI capabilities.
Job Requirements
* Bachelor's, master's or Ph.D. degree in Mathematics, Economics, Physics, Computer Science, or equivalent.
* Typically, 1-2 years of relevant work experience.
* Experience with Python and PySpark Programming : coding skills in Python for data manipulation, model development, and integration with Azure services.
* Exposure to Azure Services : Knowledge of key Azure services like Azure Machine Learning, Azure AI Search, and Azure Functions for deploying RAG systems.
* Exposure to Databricks : Ability to design, develop, and optimize workflows in Azure Databricks for data processing and feature engineering.
* Understanding of NLP and GenAI concepts : Familiarity with Large Language Models, prompt engineering for LLMs, vector databases, and Retrieval Augmented Generation (RAG) systems.
* Knowledge in Applied Statistics and Algorithms : Use statistics, mathematics, algorithms, and programming to address business challenges.
* Familiarity with the deployment and scaling of RAG systems in production, using Azure’s containerization options such as Docker, AKS (Azure Kubernetes Service), or Azure Functions.
* Ability to define and create ML models to pull valuable insights, predictions, and innovation from data.
* Familiarity with popular AI frameworks, libraries, and tools.
Knowledge & Skills
* Proper understanding of digital industry trends (RPA, AI, ML) and their benefits.
* Good communication skills (i.e. written, verbal, presentation) and collaboration skills to work effectively within a multidisciplinary team.
* Problem-solving skills and ability to think creatively to tackle complex AI challenges.
* Ability to work in globally distributed virtual teams.
* Proficiency in English is a must.
* Ability to work effectively with both technical and non-technical stakeholders to ensure successful AI / ML implementation.
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