Salesforce AI Core Engineer

Packaged/SaaS Application Engineer

Project Role : Packaged/SaaS Application Engineer
Project Role Description : Configure and support packaged or SaaS applications to adapt features, manage releases, and ensure system stability. Use standard tools, APIs, and low-code platforms to align solutions with business needs while preserving compatibility and performance.
Must have skills : Agentforce
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
AI Powered Tech Talent. As a Salesforce AI Core Engineer, you will lead the design, build, and configuration of AI-powered services and integrations that support Salesforce and adjacent enterprise applications. You will act as the primary technical point of contact for your workstream, guiding a small team in implementing Generative AI, LLM-based features, and intelligent automation using Salesforce, as well as cloud AI services from AWS, Azure, and Google Cloud. This role combines strong hands-on development skills with emerging AI engineering practices, including RAG pipelines, prompt orchestration, and AI-assisted development workflows

Roles & Responsibilities:
Lead design and implementation of core AI services (APIs, microservices, orchestration layers) consumable by Salesforce and other enterprise applications.
Build and integrate Generative AI features for chat, summarization, recommendations, and reasoning workflows using LLMs from providers such as Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Implement Retrieval-Augmented Generation (RAG) pipelines leveraging both Salesforce and non-Salesforce data sources.
Design and configure Salesforce extensions (Apex, Flows, Lightning Web Components) that consume AI services and expose intelligent capabilities to end users.
Develop and maintain orchestration logic for prompts, tools, and agent workflows, ensuring robust error handling and observability.
Apply AI guardrails such as toxicity filtering, PII masking, and policy-based response controls within AI workflows.
Use AI-assisted engineering tools (e.g., GitHub Copilot, Copado AI, Cursor, Claude) to accelerate development, improve code quality, and support testing and documentation.
Review designs and code from team members, enforce development standards, and ensure alignment with architecture guidelines.
Collaborate with architects, product owners, and business analysts to translate requirements into scalable AI designs and technical tasks.
Support estimation, sprint planning, and risk identification for the AI-related backlog.
Contribute to reusable assets such as prompt templates, evaluation frameworks, and reference implementations.

Professional & Technical Skills:
Must Have Skills
Strong hands-on experience in backend development (Java, Python, or similar) and API/microservices design.
Hands-on experience with:
o Apex
o Triggers
o Lightning Web Components (LWC)
o Salesforce Flows
Practical exposure to Generative AI and LLMs, including prompt engineering, token/embedding concepts, and context window management.
Experience integrating with at least one major cloud AI platform (Azure OpenAI, AWS AI/ML services, or Google Cloud AI).
Understanding of RAG architectures, semantic search, and vector databases.
Knowledge of Salesforce data model, security model, and integration patterns (REST/SOAP, event-driven).
Familiarity with CI/CD, version control, and automated testing practices.
Ability to guide junior developers technically and make sound implementation decisions.
Good to Have Skills
Exposure to building internal AI platforms (prompt management, embedding pipelines, model routing).
Experience with observability tools for AI workloads (logging, tracing, evaluation dashboards).

Certifications Required
Salesforce Platform Developer I – Mandatory
Salesforce Administrator – Mandatory
Salesforce AI Associate – Preferred
Agentforce Specialist – Preferred
At least one cloud certification focused on AI/ML (Azure, AWS, or Google Cloud) – Preferred.


Additional Information:
5–7 years of Salesforce development experience including exposure to AI/GenAI projects.
Experience working in Agile delivery environments.
Exposure to enterprise Salesforce implementations.
15 years of full-time education is required.