Senior AI Engineer - Salesforce & Applied Intelligence

ZoomInfo•Vancouver, WA

48d•Hybrid

About The Position

We're transforming how Go-To-Market (GTM) teams - across Sales, Marketing, and Customer Experience - leverage AI to drive smarter, faster, and more predictive decisions. Our mission is to build intelligent systems that amplify human productivity, automate repetitive processes, and deliver actionable insights directly within Salesforce. As a Senior AI Engineer, you'll design, develop, and deploy enterprise-grade AI solutions that integrate deeply with Salesforce and GTM ecosystems. You'll own the full AI development lifecycle - from model design to deployment - ensuring our solutions are scalable, secure, and seamlessly embedded into Salesforce workflows. This is a hands-on engineering role where you'll work closely with product, data, and GTM operations teams to bring cutting-edge AI capabilities - predictive analytics, NLP, and generative automation - into core business processes.

Requirements

Strong understanding of Salesforce architecture, including data schema, security model, and API integrations.
Hands-on experience with Salesforce development (Apex, LWC, Flow, SOQL/SOSL) and middleware platforms (MuleSoft, Boomi).
Advanced proficiency in Python, Java, or Node.js, including experience with AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
Knowledge of LLMs, NLP, and prompt engineering for conversational AI and intelligent automation.
Deep experience with data engineering-ETL, data lakes, feature stores, and pipeline orchestration tools.
Proficiency in building microservices and API-driven architectures for integrating AI workloads into enterprise applications.
Understanding of AIOps, MLOps, and DataOps best practices, including monitoring, CI/CD, and version control for AI models.
Experience deploying AI workloads on cloud AI platforms such as AWS Sagemaker, Azure ML, or GCP Vertex AI.
Familiarity with Salesforce Einstein AI, AI Cloud, Data Cloud, and CRM Analytics for native AI enablement.
6-10 years of hands-on experience in AI engineering, data science, or Salesforce software development.
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related quantitative field.
Proven experience integrating AI models into Salesforce or similar enterprise platforms.
Strong understanding of cloud architecture, containerization, and DevOps principles
Excellent debugging, performance tuning, and problem-solving skills.
Ability to collaborate effectively in a global, cross-functional engineering environment.
Passion for exploring new AI frameworks, LLM architectures, and Salesforce AI innovations.

Responsibilities

Design, develop, and deploy scalable AI and ML solutions that integrate directly with Salesforce and other enterprise applications.
Build and operationalize custom AI models for predictive analytics, NLP, and generative AI, embedding them into Salesforce workflows, Einstein bots, and Apex services.
Engineer robust data pipelines and ETL processes to prepare structured and unstructured data for AI model training and inference.
Implement end-to-end MLOps pipelines using tools such as Kubeflow, MLFlow, AutoML, or Vertex AI for continuous training, testing, and deployment.
Extend Salesforce functionality through Apex, Lightning Web Components (LWC), and Salesforce APIs, integrating AI services via RESTful and event-driven architectures.
Develop proof-of-concept prototypes to validate new AI models and Salesforce integrations, rapidly iterating from idea to minimum viable product (MVP).
Leverage Salesforce Einstein AI and AI Cloud capabilities to enable predictive scoring, intelligent automation, and conversational interfaces.
Integrate external AI services and LLMs (e.g., OpenAI, Anthropic, or Hugging Face) into Salesforce environments for custom generative experiences.
Partner with Salesforce administrators, data engineers, and DevOps teams to ensure AI applications are performant, compliant, and secure.
Optimize runtime performance and cost efficiency of deployed AI models using cloud-native infrastructure and containerization (Docker, Kubernetes).
Conduct code reviews, establish best practices for AI/ML engineering, and mentor junior developers in applied AI and Salesforce integration.