Senior AI/ML Engineer

Summary

Join Nerdery as a Senior AI/ML Engineer and design, implement, and deploy AI/ML solutions on Google Cloud Platform (GCP). You will define architectures, develop machine learning models, and collaborate with various teams to solve complex challenges. This role requires strong technical expertise and excellent communication skills to work with both technical and non-technical audiences. You will act as a trusted advisor throughout the sales process, leveraging Google's resources to deliver innovative solutions. The position involves working in a dynamic environment and contributing to business growth by showcasing GCP's AI/ML capabilities. This is an opportunity to make a significant impact and be part of a team that values innovation and growth.

Requirements

• Bachelor’s Degree is Computer Science or related field
• 5+ years of experience in machine learning or AI engineering, with at least 3+ years on GCP
• Strong understanding of AI/ML algorithms, model training, and evaluation techniques
• 3+ years proficiency in Python and experience with AI/ML frameworks like TensorFlow or PyTorch
• Experience in developing reusable tooling (libraries, modules, etc.) using object-oriented design to manage code complexity alongside willingness to learn object-oriented design principles, as necessary
• Experience in and familiarity with MLOps principles, including, but not limited to, model registries, model serving (batch and real-time), model versioning, promotion, model and feature drift, and automated re-training
• Deep understanding of Machine Learning algorithms, techniques and methodologies with hands-on experience in applying supervised, unsupervised and deep learning techniques to real-world problems
• Experience with GCP AI/ML services like Vertex AI, AutoML, and BigQuery ML
• Experience with various DevOps functions and technologies (i.e. IaC with Terraform, CI/CD, automation tooling, etc.)
• Familiarity with MLOps tools and practices for model deployment, monitoring, and management
• Excellent communication and interpersonal skills, with a consultative approach
• Ability to work independently and as part of a team in a fast-paced environment
• Must be legally authorized to work within the country of employment without sponsorship for employment visa status

Responsibilities

• Lead the end-to-end design, development, and deployment of enterprise-level Machine Learning solutions by building and integrating AI/ML pipelines using Google Cloud's Vertex AI platform and other relevant tools, ensuring seamless data collection, preprocessing, model training, evaluation, and alignment with the overall data architecture
• Provide thought leadership and technical expertise in designing, building and optimizing end-to-end data pipelines, from data extraction and transformation to loading and visualization
• Leverage AutoML, Gemini, and other pre-built AI solutions to deliver quick wins and demonstrate value to clients
• Collaborate with business stakeholders, data engineers, data scientists and other teams to understand data requirements, use cases, business objectives and integrate data pipelines with AI/ML models
• Implement MLOps practices to ensure the reliability, scalability, and maintainability of AI solutions
• Develop compelling customer demos and workshops showcasing the capabilities of GCP's AI/ML services alongside the Solutions Architect
• Collaborate with the Solutions Engineering team to develop and maintain Infrastructure as Code (IaC) for AI/ML models
• Create and maintain technical documentation, run books, code and best practices for data and machine learning engineering on the GCP platform
• Contribute to the productization of AI/ML solutions, enabling seamless integration and deployment
• Provide client presentations to review project design, outcomes and recommendations
• Participate in client workshops and meetings, providing technical expertise and guidance on AI/ML capabilities
• Stay current on the latest AI/ML trends and technologies, particularly within the GCP ecosystem

Preferred Qualifications

• Google Cloud Professional Cloud Machine Learning Engineer and/or Cloud Engineer
• Experience with GCP security tools and best practices
• Experience in data architecture, data engineering, and analytics in areas such as performance optimization, pipeline integration, infrastructure configuration, etc
• Experience with containerization technologies like Docker and Kubernetes
• Familiarity with Agile methodologies