Description

You design, develop, and deploy scalable AI and ML solutions that integrate with Salesforce and enterprise applications.

This role is hybrid.

Responsibilities

• Build and operationalize custom AI models, including predictive analytics, NLP, and 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 management.
• 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.
• Experience with TensorFlow, PyTorch, or scikit-learn.
• Deep understanding of Salesforce architecture, including data schema, security model, and API integrations.
• Hands-on 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.

Preferred Skills

• Experience integrating external AI services and LLMs (e.g., OpenAI, Hugging Face) into Salesforce.

Key Skills

Education

Any Graduate