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AI / ML Engineer
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AI / ML Engineer
Accenture5-7 Years
- Posted a day ago
- Be among the first 10 applicants
Job Description
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Agentforce, Salesforce Agentforce
Good to have skills : Salesforce Einstein AI, Prompt Engineering, Agent Development
Minimum 5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
AI Agent Developer – Salesforce
Role Overview
The AI Platform Developer builds and runs the AI capability layer that empowers the global commercial team. This role creates agents, automations, and AI-enabled workflows that are embedded directly into the systems sellers use every day — making AI actionable rather than aspirational.
THE OBJECTIVE
Develop, deploy, and govern AI agents, prompt libraries, and automation workflows for the commercial organization, with outputs written back into CRM systems to create measurable seller productivity gains.
Key Responsibilities
Build and deploy AI agents in Salesforce using Agentforce for use cases including prospecting, account research, and call-transcript intelligence
Develop governed prompt libraries and reusable AI components approved for commercial use
Design and implement automations that eliminate manual effort from seller and SDR workflows
Ensure AI outputs are written back into CRM objects (Salesforce) so insight is actionable and trackable
Implement Responsible AI compliance standards covering data handling, retention, and approved use
Partner with Sales, RevOps, and IT to identify high-value automation opportunities
Maintain and iterate on deployed agents based on usage analytics and seller feedback
Document AI components, prompt templates, and governance policies for the commercial org
Skills & Experience Required
4–7 years of experience with Salesforce development and the broader Salesforce ecosystem
Hands-on experience with Agentforce and Copilot Studio for agent development
Proficiency in prompt engineering, APIs, and AI/ML integration patterns
Experience With Data Modeling To Support AI Input/output Requirements
Commercial context and understanding of GTM workflows — this role ships working tools, not pure AI research
Ability to implement responsible AI guardrails (PII handling, output auditing, retention policies)
Strong Collaboration Skills Across Sales, IT, And Operations Functions
NICE TO HAVE
Experience with LLM orchestration frameworks (LangChain, Semantic Kernel, or equivalent)
Background in SDR/AE workflows, sales enablement, or revenue operations
Salesforce certifications (Platform Developer, AI Specialist)
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Agentforce, Salesforce Agentforce
Good to have skills : Salesforce Einstein AI, Prompt Engineering, Agent Development
Minimum 5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
AI Agent Developer – Salesforce
Role Overview
The AI Platform Developer builds and runs the AI capability layer that empowers the global commercial team. This role creates agents, automations, and AI-enabled workflows that are embedded directly into the systems sellers use every day — making AI actionable rather than aspirational.
THE OBJECTIVE
Develop, deploy, and govern AI agents, prompt libraries, and automation workflows for the commercial organization, with outputs written back into CRM systems to create measurable seller productivity gains.
Key Responsibilities
Build and deploy AI agents in Salesforce using Agentforce for use cases including prospecting, account research, and call-transcript intelligence
Develop governed prompt libraries and reusable AI components approved for commercial use
Design and implement automations that eliminate manual effort from seller and SDR workflows
Ensure AI outputs are written back into CRM objects (Salesforce) so insight is actionable and trackable
Implement Responsible AI compliance standards covering data handling, retention, and approved use
Partner with Sales, RevOps, and IT to identify high-value automation opportunities
Maintain and iterate on deployed agents based on usage analytics and seller feedback
Document AI components, prompt templates, and governance policies for the commercial org
Skills & Experience Required
4–7 years of experience with Salesforce development and the broader Salesforce ecosystem
Hands-on experience with Agentforce and Copilot Studio for agent development
Proficiency in prompt engineering, APIs, and AI/ML integration patterns
Experience With Data Modeling To Support AI Input/output Requirements
Commercial context and understanding of GTM workflows — this role ships working tools, not pure AI research
Ability to implement responsible AI guardrails (PII handling, output auditing, retention policies)
Strong Collaboration Skills Across Sales, IT, And Operations Functions
NICE TO HAVE
Experience with LLM orchestration frameworks (LangChain, Semantic Kernel, or equivalent)
Background in SDR/AE workflows, sales enablement, or revenue operations
Salesforce certifications (Platform Developer, AI Specialist)
More Info
Key Skills
AI tools
GenAI models
Cloud AI services
Responsible AI compliance
Agentforce
chatbots
Salesforce Einstein AI
AI ML integration patterns
Prompt Engineering
About Company
Skills:
Neural network architectures, On-premises infrastructure, Generative AI model integration, Deep learning frameworks, Chatbots, Cloud AI services, Image processing systems
Skills:
error analysis , Machine Learning, Natural Language Processing, Deep Learning, Numpy, Pytorch, Pandas, Python, Hugging Face Transformers, PDF parsing frameworks, Model evaluation metrics, TesserAct, Scikit-Learn, LayoutLM, Document OCR processing tools, PaddleOCR, Transformer-based architectures, Statistical Sampling
Skills:
Modern software engineering practices, semantic search, Generative AI, Embeddings, AI model evaluation, AI application architecture, AI Agents, Prompt optimization, Vector databases, AI safety best practices, Prompt Engineering