Job Description
Gaude is seeking a highly capable Senior AI/ML Engineer with 4 5 years of hands-on experience in designing, building, and deploying AI-driven solutions in enterprise environments. This role demands strong applied machine learning expertise combined with solid data engineering fundamentals and cloud platform experience.
You will be responsible for building scalable AI systems, production-ready ML pipelines, and robust data workflows that support analytics, automation, and intelligent business applications. The ideal candidate combines strong technical depth with business understanding and the ability to work effectively in cross-functional teams.
Key Responsibilities
Design, develop, and maintain scalable data pipelines and enterprise data models supporting analytics, AI, and business intelligence use cases.
Build and optimize ETL/ELT workflows using Azure Data Factory (ADF) or equivalent cloud-native integration tools.
Develop and manage cloud-based data platforms, including Snowflake, to enable high-performance and cost-efficient analytics.
Design and implement AI/ML models including deep learning, NLP applications, predictive analytics models, and LLM-based solutions.
Contribute to the development of generative AI use cases and Retrieval-Augmented Generation (RAG) workflows where applicable.
Perform data preprocessing, feature engineering, model training, validation, tuning, and performance optimization.
Develop and deploy end-to-end ML pipelines covering experimentation, versioning, deployment, and monitoring.
Integrate ML models into production systems through APIs and microservices architecture.
Implement CI/CD pipelines to automate data and ML workflows ensuring reliability and faster releases.
Build dashboards and analytical applications using Tableau or Power BI to deliver actionable insights to business stakeholders.
Collaborate with engineering, product, analytics, and business teams to translate requirements into scalable AI solutions.
Ensure adherence to best practices in data governance, security, quality, observability, and documentation.
Continuously evaluate emerging AI technologies and contribute to innovation initiatives within the organization.
