You design, develop, and deploy scalable AI and ML solutions integrating with Salesforce and enterprise applications.
Responsibilities
Build and operationalize custom AI models (predictive analytics, NLP, generative AI), embedding them into Salesforce workflows, Einstein bots, and Apex services.
Engineer data pipelines and ETL processes to prepare structured and unstructured data for model training and inference.
Implement end-to-end MLOps pipelines using tools like Kubeflow, MLFlow, or Vertex AI for continuous model lifecycle.
Extend Salesforce functionality using Apex, Lightning Web Components (LWC), and Salesforce APIs, integrating AI services via RESTful architectures.
Develop proof-of-concept prototypes to validate new AI models and Salesforce integrations, iterating to MVP.
Required Skills
6+ years of hands-on experience in AI engineering, data science, or Salesforce development.
Advanced proficiency in Python, Java, or Node.js, with experience in TensorFlow, PyTorch, or scikit-learn.
Deep understanding of Salesforce architecture, including data schema, security model, and API integrations.
Experience with Salesforce development (Apex, LWC, Flow, SOQL/SOSL).
Knowledge of LLMs, NLP, and prompt engineering for conversational AI.
Proficiency in building microservices and API-driven architectures for AI workloads.
Experience with MLOps concepts, including monitoring, CI/CD, and version control for AI models.
Familiarity with cloud AI platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI.
Experience integrating external AI services and LLMs (e.g., OpenAI, Hugging Face) into Salesforce.