Found.dev
Role Highlights
Languages used
Python
SQL
Key skills
Deep Learning
NLP
Data Collection
Feature Engineering
Prompt Engineering
Machine Learning
ML Ops
Data Science
Vector Database
RAG
API
LLMs
Generative AI
Deployment
ETL
ELT
Architecture
Modelling
Regression
Clustering
Search
Optimization
Kernel
Hosting
Cloud
Transformation
DataSets
Testing
Operations
Tools, Libraries and Frameworks
HuggingFace
Rest
DataBricks
MLFlow
OpenAI
Azure
AWS
GCP
IBM
Git
Langchain
Description
The role involves leading end-to-end data science projects from initial ideation through to final deployment. The individual will design and implement machine learning and natural language processing algorithms to address complex business problems. Responsibilities include building scalable, governed, and secure applications using advanced frameworks and cloud architectures. The position requires collaborating with cross-functional teams to deliver actionable insights and drive project impact. Additionally, the role focuses on maintaining model reproducibility and ensuring effective communication of technical outcomes to various stakeholders.
Required Qualifications and Skills
The role requires seven to nine years of hands-on experience in data science, machine learning, deep learning, and natural language processing within production environments. Candidates must possess at least three years of applied experience specifically in generative AI and large language models. Proficiency in end-to-end Databricks implementation, agentic AI development, and cloud-based machine learning pipelines is mandatory. Strong programming skills in Python and SQL are required, alongside experience with MLOps, model deployment, and version control systems.
Disclaimer
Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.
About the company
IBM
Description
IBM infuses core business operations with intelligence, from machine learning to generative AI, to make organizations more responsive, productive, and resilient. It helps clients put AI into action now, creating real value with trust, speed, and confidence across various areas like digital labor, IT automation, and security. The ability to utilize all data is critical, as AI's effectiveness is dependent on the quality of data fueling it, with IBM's AI, and data platform aiming to scale and accelerate AI's impact with trusted data. IBM's hybrid cloud platform offers a comprehensive approach to development, security, and operations across hybrid environments, laying a flexible foundation for leveraging data wherever it resides.
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