15h Left Systems Architect, Artificial Intelligence

Job Description

- AGCO is looking to hire candidates for the position of Systems Architect, AI Systems
- We are seeking an experienced and innovative Systems Architect, AI Systems to lead the design, creation, and evolution of system architectures for AI solutions within our organization
- The Systems Architect, AI Systems, is a critical role responsible for designing, implementing, and evolving scalable AI architectures that support and drive the company's AI and data strategies
- The ideal candidate will have extensive experience in architecting, deploying, and managing AI solutions in cloud-based environments, with a focus on high performance, security, and interoperability
- This role will work closely with data science, engineering, and delivery teams to ensure AI systems are seamlessly integrated across the organizations operations
- Your Impact
- AI Systems Architecture: Design and implement scalable architectures that support AI/ML models and data systems, optimizing for performance, cost-efficiency, and security
- Cloud Platform Expertise: Leverage cloud environments (AWS, GCP, Azure) to architect solutions that support high-volume data processing, storage, and AI model deployment
- Data Integration & Pipeline Design: Develop and oversee data pipelines that ensure smooth data flow between various sources and AI systems, incorporating best practices for ETL, data lakes, and real-time streaming
- Model Deployment and Monitoring: Collaborate with ML engineers to establish CI/CD pipelines for model deployment, monitoring, and lifecycle management to ensure models remain reliable and efficient in production
- Tool Evaluation and Adoption: Evaluate and recommend AI tools, libraries, and platforms that align with business goals, ensuring that the chosen technology stack can support complex AI and data requirements
- Security and Compliance: Ensure all AI architectures meet security, regulatory, and compliance standards relevant to data handling, model deployment, and user access, particularly with regards to GDPR, CPRA, and industry-specific regulations
- Documentation and Best Practices: Establish and maintain comprehensive documentation of architecture designs, policies, and processes, promoting best practices across the AI & Data Operations organization
- Functional Knowledge
- Advanced AI and Data Systems: Deep understanding of AI architectures, including distributed computing frameworks (e g, Apache Spark, Hadoop), data lakes, data warehousing, and MLOps
- Cloud Infrastructure and Services: Proficient in AWS (SageMaker, Redshift, Lambda), Google Cloud Platform (BigQuery, Vertex AI), and/or Azure AI services, with a strong grasp of cloud-based data management and ML lifecycle management
- Containerization and Orchestration: Skilled in Docker, Kubernetes, and other container management tools to ensure scalability and efficiency in model deployment and orchestration
- Data Security and Governance: Familiar with data governance frameworks and security protocols, including encryption, authentication, and compliance requirements (GDPR, CPRA, etc ), especially as they relate to cloud-based AI solutions
- Business Expertise
- Experience in industries such as manufacturing, agriculture, or supply chain, particularly in AI and data use cases
- Familiarity with regulatory requirements related to data governance and security
- Experience with emerging technologies like edge computing, IoT, and AI/ML automation tools
- Your Experience And Qualifications
- Bachelors degree in computer science, data science, or related field
- Masters degree or relevant certifications is preferred
- Experience: 8+ years of experience in systems architecture, with at least 5 years focused on AI and data systems in a cloud environment
- Programming Languages: Proficient in Python, Java, and SQL; familiarity with R, Scala, or other languages is a plus
- MLOps Tools: Hands-on experience with MLOps tools and frameworks (e g, MLflow, Kubeflow, Airflow) for model tracking, versioning, and deployment
- Data Engineering: Strong experience with ETL processes, real-time data streaming (Kafka, Spark Streaming), and data pipeline design
- Analytical Skills: Strong problem-solving abilities with the capacity to analyze system requirements and design scalable solutions that meet both technical and business needs
- Communication and Leadership: Excellent communication skills to explain complex architecture concepts to non-technical stakeholders, as well as the ability to lead technical discussions and mentor junior team members
- Certifications: Relevant certifications are a plus (e g, AWS Certified Solutions Architect, Google Professional Data Engineer, Certified Kubernetes Administrator)
- Your Benefits

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