Senior Consultant – Enterprise AI (Data Background)

DynPro Inc.; San Jose (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

A technology consulting firm is seeking a Senior Consultant – Enterprise AI in San Jose, California. The role focuses on designing AI-driven solutions enhancing enterprise functions such as HR and Finance, emphasizing data-driven problem solving and AI applications. Candidates should have 7–8 years of experience in Data Analytics or Data Science and be proficient in Python. Experience with GenAI applications and RAG pipeline development is essential for this position.

Qualifications

• 7–8 years of experience in Data Analytics, Data Science, or AI Consulting roles.
• Strong background in data analysis and problem-solving.
• Hands-on experience with GenAI applications in enterprise.
• Experience with RAG pipelines and enterprise knowledge bases.
• Proficiency in Python for data analysis and AI workflows.

Responsibilities

• Design and implement AI-powered solutions for enterprise functions.
• Translate business problems into AI use cases.
• Build and optimize RAG pipelines using enterprise data.
• Work with structured and unstructured data to enable intelligent insights.
• Collaborate with stakeholders to identify opportunities and deliver solutions.

Skills

Data Analytics

AI Consulting

Data Science

Statistical Modeling

Python for Data Analysis

RAG Pipeline Development

Communication Skills

Tools

LangChain

LlamaIndex

Semantic Kernel

Azure OpenAI

AWS Bedrock

Vertex AI

Job description

We are looking for a Senior Consultant – Enterprise AI with a strong data-focused background (Data Analyst / Data Scientist) to design and deliver AI-driven solutions across enterprise functions. This role emphasizes leveraging LLMs, data insights, and enterprise knowledge systems to build intelligent copilots and automation solutions that enhance productivity and decision-making.

This is not a pure development role, the focus is on data-driven problem solving, AI application design, and business use-case delivery.

Key Responsibilities

• Design and implement AI-powered copilots and automation solutions for enterprise functions such as HR, IT, Finance, and Operations.
• Translate business problems into data-driven AI use cases and scalable solutions.
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines using enterprise data sources.
• Work with structured and unstructured data to enable intelligent search, insights, and decision support.
• Apply LLMs, prompt engineering, and agentic workflows to solve real-world business problems.
• Collaborate with cross-functional stakeholders to identify high-impact opportunities and deliver solutions.
• Ensure solutions follow data governance, security, and compliance standards.
• Monitor and evaluate model performance, user adoption, and business impact.
• Contribute to best practices in LLMOps, prompt management, and model evaluation.

Required Qualifications

• 7–8 years of experience in Data Analytics, Data Science, or AI Consulting roles.
• Strong background in data analysis, statistical modeling, and data-driven problem solving.
• Hands‑on experience with GenAI / LLM applications in enterprise use cases.
• Experience building RAG pipelines and working with enterprise knowledge bases.
• Proficiency in Python for data analysis and AI workflows (not heavy backend development).
• Experience with tools/frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Familiarity with cloud AI platforms (Azure OpenAI, AWS Bedrock, Vertex AI, etc.).
• Strong understanding of data governance, privacy, and enterprise security concepts.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Experience in enterprise consulting environments.
• Exposure to agentic AI workflows, tool/function calling.
• Familiarity with enterprise platforms like ServiceNow, Workday, Microsoft 365, or similar.
• Experience with data storytelling, dashboards, and business insights delivery.
• Knowledge of LLMOps, evaluation frameworks, and AI governance practices.