AI Architect
Forsyth Barnes - Milwaukee, WI
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Role: AI ArchitectAs an AI Architect, your primary responsibility will be to design, develop, and implement comprehensive solutions in data science and machine learning (ML), incorporating statistical modeling, ML, deep learning, and generative AI (GenAI). You will also establish and implement MLOps and AIOps processes to streamline product lifecycles. Additionally, you will oversee the technical framework of AI systems, ensuring best practices in model development, deployment, monitoring, and maintenance. As a senior team member, you'll review the work of colleagues, guide design/code reviews, and mentor team members on industry best practices for creating AI solutions.Key Responsibilities:AI Solution Architecture: Design end-to-end AI solutions tailored to address specific business requirements (e.g., Finance, Marketing, Supply Chain, Digital Experiences), choosing appropriate algorithms, models, and technologies for each application.Technical Leadership: Provide expert-level guidance to data scientists, ensuring alignment with coding standards, cutting-edge algorithms, and architectural principles.Architectural Blueprinting: Define the overall architecture for AI systems, including data pipelines, model training workflows, inference frameworks, and integration with existing IT infrastructures.Model Development and Refinement: Lead the creation and optimization of machine learning models, ensuring they are high-performing, scalable, and interpretable.Technology Exploration: Evaluate and recommend the latest AI tools, technologies, and frameworks that best align with project needs.Collaboration with Product Teams: Work closely with product managers to identify and build fit-for-purpose model architectures. Communicate intricate AI modeling approaches clearly to various stakeholders.Cross-functional Teamwork: Partner with domain experts, data engineers, and software developers to integrate AI capabilities into applications and business processes.Generative AI Expertise: Leverage deep expertise in generative AI frameworks to create scalable, high-performance AI solutions using cutting-edge AI advancements.MLOps and AIOps Setup: Implement MLOps/AIOps best practices to manage the entire lifecycle of ML and LLMs, including automation, versioning, CI/CD pipelines, and model monitoring.Research & Innovation: Keep abreast of the latest advancements in AI and continuously enhance the performance and capabilities of AI solutions.Performance & Cost Optimization: Improve AI models for efficiency, speed, and accuracy using techniques such as hyperparameter tuning, model compression, and deployment strategies to ensure cost-effectiveness and scalability.Security & Compliance: Ensure AI systems adhere to data protection regulations, security standards, and ethical guidelines, working closely with security teams.Requirements:Master's degree in Data Science, AI, Machine Learning, or a related field (Ph.D. preferred).8-10 years of hands-on experience in statistics, engineering, machine learning, or AI, with a strong focus on AI architecture, GenAI technologies, and MLOps.Proficient in programming languages like Python, R, PySpark, or Scala, with deep experience using AI frameworks (e.g., TensorFlow, PyTorch) and GenAI-specific tools (e.g., Retrieval-Augmented Generation frameworks).Extensive understanding of MLOps concepts including versioning, automated workflows, deployment strategies, and continuous model monitoring.Strong analytical skills, with the ability to break down complex technical ideas and communicate them effectively to different stakeholders.Preferred: Certifications in AI, ML, or Generative AI platforms.If you're passionate about driving innovation in AI architecture and MLOps practices, this position offers a dynamic and collaborative environment for excelling in Generative AI and advanced ML solutions.Salary$120,000 - $156,000 (dependent on experience, education, and location)BenefitsMedical, Dental, Vision, 401kSponsorshipAvailable
Created: 2024-10-11