AI Engineer

Job Description:

AI Agent & Automation Development

• Design and develop AI Agents for internal enterprise use and client-facing applications (RAG, chatbots, automation agents).
• Build automated data pipelines: collection, cleansing, enrichment, and distribution powered by AI.
• Develop intelligent alerting systems with built-in root cause analysis and recommended actions.
• Collaborate with the Salesforce CRM team on system integrations between CRM and other platforms (Call Center, DMS, Social Platform) to ensure seamless data exchange and end-to-end customer journey tracking.

Data Team Support

• Collaborate with the Data team to add AI layers on top of existing dashboards and analytics tools.
• Build AI tools to automate data processing workflows and quality validation.
• Advise on and implement AI techniques to improve reporting accuracy and speed.

Internal AI Strategy

• Evaluate and recommend AI models and frameworks suited to each real-world business use case.
• Train and support team members in applying AI to their daily workflows.
• Monitor emerging AI developments and apply relevant techniques to production systems.

Requirements

• Minimum 2-3 years of hands-on AI/ML development experience in production environments.
• Proven track record designing and deploying at least one AI Agent or RAG system in a real project.
• Proficient in Python at production level; capable of writing and reviewing high-quality code.
• Strong understanding of the LLM ecosystem: OpenAI, Anthropic, Gemini, Llama, and advanced prompting techniques.
• Experience with vector databases (Pinecone, Weaviate, Chroma, pgvector).
• Working knowledge of Salesforce CRM: data model, automation flows, or API integration.
• Demonstrated application of AI to solve real business problems; able to present work via portfolio or case study.

Preferred

• Experience with Langchain, LlamaIndex, CrewAI, or equivalent AI agent frameworks.
• Familiarity with Salesforce Einstein, Agentforce, or AI integration on the Salesforce platform.
• Experience with MLOps: model serving, monitoring, and A/B testing.
• Portfolio of production AI projects available for review (GitHub, demo, case study).

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