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 support high-volume data mining, data analytics, and machine learning/AI-based analysis.
• Ensure seamless integration of AI models into production environments through model training, evaluation, deployment, and scalability testing on cloud platforms such as AWS.
• Maintain comprehensive documentation of workflows, model evaluations, and system configurations to support ongoing optimization efforts.
• Map current-state workflows and identify specific steps where AI can plausibly reduce time, cost, or error rate
• Design the future-state, AI-enabled version of the workflow, choosing the right build approach for the use case’s complexity and risk
• Build working pilots/proofs-of-concept directly
• Define efficiency and quality metrics for each pilot up front and instrument the pilot to capture them
• Run structured evaluations and produce a clear go/no-go recommendation
• Communicate workflow findings and pilot results in plain language to non-technical program stakeholders, including clear visuals of before/after impact

Experience

• Proven experience working with cloud-based machine learning services such as AWS SageMaker or Google Cloud AI platforms.
• Strong proficiency in programming languages including Python, Java, C, VBA, Bash (Unix shell), with a focus on developing scalable AI solutions.
• Extensive knowledge of big data technologies like Hadoop, Spark implementation, Talend ETL tools, and SQL databases for managing large datasets efficiently.
• Hands-on experience with statistical modeling techniques including unsupervised learning methods such as clustering or dimensionality reduction.
• Familiarity with natural language processing (NLP), generative AI models, and predictive modeling analysis to enhance AI capabilities.
• Ability to design database schemas focusing on linked data principles for efficient data retrieval and integration across multiple sources.
• Strong understanding of model evaluation metrics for AI models alongside expertise in statistical analysis tools like R or SAS for research purposes.
• Hands-on build experience with no-code/low-code AI tools (Microsoft Copilot Studio, Power Platform AI Builder, Retool AI) and enough Python to script a lightweight automation, data-prep step, or basic RAG pipeline
• Practical understanding of LLM/RAG patterns (embeddings, retrieval, prompt design) sufficient to build a working pilot, plus basic vector-database familiarity (pgvector, FAISS, or a managed equivalent)
• Workflow/process mapping tools (Lucidchart, Miro,…