Lead Solutions Engineer– Back Office Transformation
$114,800–$213,200 year
On-siteSan Jose, California, United States
Job Summary
Partner with HR, Legal, Finance, and Marketing teams to identify workflow pain points and determine whether solutions require process cleanup, standard automation, AI-assisted workflows, or agentic development. Design, prototype, test, and deploy practical solutions that improve efficiency, visibility, and cycle time while making tradeoff decisions based on business impact, cost, and maintainability. Document solution logic and assumptions for both technical and non-technical stakeholders, then formalize specifications for long-term support or handoff to IT. Operate independently on technical investigations and solution development, proactively communicating progress and risks. Establish a repeatable approach for future transformation work, including opportunity intake, prioritization, and prototype-to-production handoff.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
- 3–5 years of relevant experience, or equivalent experience through graduate-level work
- Strong Python programming skills and experience building practical tools, automations, or data-driven applications
- Working knowledge of Large Language Models, Generative AI, prompt engineering, and Retrieval-Augmented Generation concepts including embeddings, search, generation, and caching
- Interest in or exposure to agentic development patterns, including workflows that use models, tools, memory, or external systems
- Familiarity with APIs, MCP, or other methods for connecting models and applications to data, tools, or business systems
- Ability to evaluate technical approaches in the context of business outcomes, user needs, maintainability, and operational constraints
- Strong communication skills to explain technical concepts to non-technical audiences
Desired Qualifications
- Direct experience taking AI, automation, or data-driven solutions from prototype to production, including testing, documentation, user feedback, and operational support considerations
- Hands-on experience with agentic frameworks or production AI agent development
- Exposure to cross-functional business environments (HR, Finance, Legal, or Marketing)
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