AI Workflow Optimization Engineer

Overview Join our innovative team as an AI Workflow Optimization Engineer and be at the forefront of transforming data-driven processes through cutting-edge artificial intelligence and machine learning technologies. In this dynamic role, you will design, develop, and implement scalable AI workflows that enhance operational efficiency and predictive capabilities across diverse systems. Your expertise will drive the deployment of AI models in cloud environments, optimize data pipelines, and contribute to groundbreaking advancements in AI implementation. If you are passionate about leveraging big data systems, statistical modeling, and AI frameworks to solve complex problems, this is your opportunity to make a significant impact in a fast-paced, forward-thinking organization.

Sits at the intersection of workflow design, hands-on AI build, and outcomes measurement. Maps how a process works today, designs and builds a working AI-enabled pilot to improve it (using no-code, low-code, or lightweight full-code tools as fit), and rigorously evaluates whether the pilot actually improved efficiency, accuracy, or cost before recommending it for wider adoption.

Responsibilities

• Develop and optimize end-to-end AI workflows utilizing machine learning frameworks such as TensorFlow, Spark MLlib, and other cloud-based AI services to ensure scalable and efficient model deployment.
• Design and implement robust ETL processes for large-scale data ingestion, transformation, and storage using tools like Talend, Hadoop, and SQL databases.
• Collaborate with data scientists and engineers to refine predictive modeling analysis, natural language processing applications, and generative AI solutions for diverse research initiatives.
• Conduct statistical analysis for research purposes using tools like R, SAS, and statistical analysis software to evaluate model performance and accuracy.
• Manage big data systems by implementing Hadoop and Spark solutions that supp