AI/ML Principal Architect (Application)
Experience: 12 Years
Role Summary:
As the AI/ML Principal Architect, you will be responsible for defining and overseeing the architectural strategy for AI and machine learning systems across our manufacturing automation portfolio. You will lead the design, implementation, and optimization of AI frameworks and models, ensuring they are scalable, efficient, and aligned with industry standards. This is a high-impact role requiring deep technical expertise, strategic thinking, and leadership capabilities to shape the company's AI vision and bring innovative products to market.
Core Responsibilities:
1. Strategic AI/ML Architecture & Vision
- Architectural Roadmap: Develop a comprehensive AI/ML architecture roadmap aligned with the company's strategic goals. Identify the right technologies, tools, and frameworks needed to build and scale AI-powered manufacturing automation solutions.
- Platform Strategy: Define the platform strategy for AI/ML deployment in manufacturing environments, with a focus on scalability, modularity, and performance. Establish guidelines for developing reusable components, libraries, and APIs to support diverse AI applications.
- Thought Leadership: Act as an internal and external AI thought leader, representing the company's AI initiatives in conferences, industry panels, and client meetings. Advocate for best practices in AI/ML architecture and contribute to the company's knowledge base.
2. AI/ML Solution Design & Development
- End-to-End Solution Design: Lead the design and development of end-to-end AI solutions for manufacturing, from data ingestion and processing to model deployment and inference. Ensure that solutions are architected to meet performance, scalability, and reliability requirements.
- Model Selection & Optimization: Guide the team in selecting appropriate machine learning and deep learning models based on project requirements. Optimize model performance through hyperparameter tuning, model compression, and deployment strategies.
- Generative AI Integration: Research and integrate generative AI models for use cases such as predictive maintenance, defect detection, and process optimization. Explore innovative applications of generative AI within manufacturing to enhance automation and provide data-driven insights.
- CUDA Optimization for Deep Learning: Leverage CUDA and GPU acceleration to optimize the training and inference of deep learning models, ensuring high-performance execution for large datasets and real-time manufacturing applications.
3. Infrastructure and Deployment
- Containerization with Docker: Design and implement containerized AI/ML solutions using Docker to ensure consistency, scalability, and ease of deployment across cloud and edge environments. Establish best practices for container orchestration and resource management.
- Cloud and Edge Deployment: Develop strategies for deploying AI models on cloud platforms (e.g., AWS, Azure, GCP) and edge devices to support real-time processing in manufacturing environments. Utilize containerization and orchestration tools like Kubernetes for scalable, multi-environment deployments.
- Data Pipeline Architecture: Design data pipelines that facilitate real-time processing, storage, and retrieval for AI models. Architect data lakes, warehouses, and streaming frameworks to handle high-volume, high-velocity data in manufacturing environments.
4. Cross-Functional Collaboration & Leadership
- Collaborate with Engineering and Product Teams: Work closely with software engineering, data science, robotics, and hardware teams to ensure seamless integration of AI components. Provide architectural guidance and support to align efforts across teams.
- Technical Leadership & Mentorship: Mentor AI/ML engineers, data scientists, and junior architects on best practices in AI architecture, model development, and deployment. Foster a culture of innovation, technical excellence, and collaboration within the team.
- Stakeholder Engagement: Engage with stakeholders, including product managers, clients, and senior leadership, to understand business requirements and translate them into technical specifications for AI/ML solutions.
5. DevOps, MLOps, and Agile Practices
- Establish MLOps Pipelines: Design and implement robust MLOps practices to automate model training, testing, deployment, and monitoring. Leverage CI/CD pipelines to streamline the AI/ML lifecycle, ensuring consistent, repeatable results in production.
- Agile Methodologies: Promote Agile methodologies within the AI/ML team to enhance adaptability and responsiveness. Lead sprint planning, retrospective sessions, and other Agile practices to ensure efficient project execution.
- DevOps Integration: Collaborate with the DevOps team to integrate AI/ML models into production environments seamlessly. Develop strategies for deploying models on edge devices and cloud platforms, with a focus on high availability and low latency.
6. Performance, Security, and Compliance
- Performance Tuning: Oversee the optimization of AI/ML algorithms and models to meet the real-time performance requirements of manufacturing automation systems. Utilize techniques such as model pruning, quantization, and distributed processing.
- Security and Privacy: Ensure that AI systems adhere to best practices in security and data privacy, particularly when handling sensitive manufacturing data. Implement measures to protect against adversarial attacks and data breaches.
- Compliance and Standardization: Align AI architecture with industry standards, regulatory requirements, and compliance guidelines. Maintain documentation and standards that support reproducibility, traceability, and auditability of AI solutions.
Required Qualifications:
- Education: Master's or Ph.D. in Computer Science, Data Science, Engineering, or a related field with a focus on AI/ML.
- Experience:
- 12+ years of experience in AI/ML, with at least 5 years in an architectural or senior technical leadership role.
- Proven track record of architecting and deploying AI/ML solutions, preferably within manufacturing, industrial automation, or a similar domain.
- Hands-on experience with a broad range of machine learning, deep learning, and data processing frameworks (e.g., TensorFlow, Keras, PyTorch, Apache Spark).
- Experience with ML tools and libraries such as scikit-learn, XGBoost, LightGBM, and Hugging Face Transformers.
- Technical Skills:
- AI/ML Expertise: Deep understanding of supervised, unsupervised, reinforcement, and generative learning techniques, as well as expertise in model evaluation, tuning, and optimization.
- CUDA and GPU Processing: Proficiency in GPU acceleration using CUDA for model training and inference optimization.
- Data Engineering: Proficiency in data pipeline design, big data processing, and storage solutions (e.g., Kafka, Hadoop, Snowflake).
- Cloud and Edge Deployment: Experience deploying AI models on cloud (e.g., AWS, Azure, GCP) and edge computing platforms. Understanding of distributed computing and containerization (Docker, Kubernetes).
- Programming Skills: Strong programming skills in Python, along with experience in Java, C++, or other relevant languages for AI and data processing.
- MLOps and DevOps: Proficiency in MLOps practices and tools (e.g., MLflow, Kubeflow, DVC) for model versioning, experiment tracking, and automated deployment. Experience with CI/CD pipelines and DevOps practices.
- Model Monitoring and Maintenance: Experience with tools and practices for monitoring model performance in production, detecting drift, and implementing automated retraining pipelines.
- A/B Testing and Experimentation: Familiarity with designing and implementing A/B testing frameworks for AI/ML models to evaluate performance improvements and new features.
- Scalability and Performance Optimization: Advanced knowledge of techniques for scaling AI/ML systems to handle large-scale data and high-throughput requirements in manufacturing environments.
- Ethical AI and Bias Mitigation: Understanding of ethical considerations in AI development and deployment, including techniques for identifying and mitigating bias in models and datasets.
- Regulatory Compliance: Knowledge of AI-specific regulations and standards relevant to the manufacturing industry, and experience in implementing compliant AI systems.
- Open Source Contributions and Management: Experience contributing to and managing open-source AI/ML projects, understanding of open-source licensing, and ability to evaluate open-source tools for integration into the company's AI stack.
- Security in AI Systems: In-depth knowledge of security best practices for AI/ML systems, including:
- Techniques for securing model training and inference pipelines
- Methods to protect against model inversion and membership inference attacks
- Strategi
