GCP AI Vertex Engineer

Responsibilities
Machine Learning Development

Maintains, as well as furthers, enhances existing machine learning modules.
Designs and implements new machine learning based approaches based on existing frameworks.
Keeps up to speed with the state of the art of academic research and AI/ML technology.
Applies industry and technology expertise to real business problems.
Data Engineering and Pipelines:

Understand business context and wrangles large, complex datasets.
Create repeatable, reusable code for data preprocessing, feature engineering, and model training.
Build robust ML pipelines using Google Vertex AI, BigQuery and other GCP services. MUST have GCP and Vertex AI experience.
Responsible AI and Fairness:

Consider ethical implications and fairness throughout the ML model development process.
Collaborate with other roles (such as data engineers, product managers, and business analysts) to ensure long-term success.
Infrastructure and MLOps:

Work with infrastructure as code to manage cloud resources.
Implement CI/CD pipelines for model deployment and monitoring.
Monitor and improve ML solutions.
Implement MLOps using Vertex AI pipelines on the GCP platform.
Process Documentation and Representation

Develops technical specifications and documentation.
Represents Perficient in the technical community, such as at conferences.
Qualifications
Experience with Google Cloud AI, Gemini, Vertex AI and BigQuery required. Experience with Agentspace preferred. Candidates who do not have Vertex AI experience will not be considered.
Strong communication skills must be able to describe and explain complex AI/ML, GenAI concepts and models to business leaders.
7 - 10 years of professional experience REQUIRED
5+ years’ Deep Learning experience REQUIRED
Desire and ability to work effectively within a group or team.
Strong knowledge of different machine learning algorithms.
Deep Learning: Proficiency in deep learning techniques and frameworks
Machine Learning: Strong understanding of traditional machine learning algorithms and their applications.
Proficiency in NLP techniques, including sentiment analysis, text generation, and language understanding models. Experience with multimodal language modeling and applications.
Neural Network Architectures: Deep understanding of various neural network architectures such as CNNs, RNNs, and Transformers.
Reinforcement Learning: Familiarity with reinforcement learning algorithms and their applications in AI.
Data Preprocessing: Skills in data cleaning, feature engineering, and data augmentation.
Model Training And Tuning: Experience in training, fine-tuning, and optimizing AI models.
Model Deployment: Knowledge of model deployment techniques, including containerization (Docker) and orchestration (Kubernetes).
Understanding of Generative AI concepts and LLM Models tailored to a wide variety of automotive applications.
Strong documentation skills for model architecture, code, and processes.
Cloud Computing: Experienced with Google Cloud Platform required.
Experience with Google Agentspace, preferred
Google GenAI L400 certification strongly preferred. 
 

Desired Skills

AI Ethics: Awareness of ethical considerations in AI, including bias mitigation and fairness.
Legal And Regulatory Knowledge: Understanding of AI-related legal and regulatory considerations, including data privacy and intellectual property.
Data Management: Proficiency in data storage and management systems, including databases and data lakes.