Forward Deployed Engineer (AI)

Nextpower•Fremont, CA

1d•$102,000 - $115,000

About The Position

This role is responsible for partnering directly with business functions across the organization to identify, design, build, and deploy AI-powered solutions that improve productivity, automate business processes, and accelerate decision-making. The ideal candidate is an engineer who enjoys solving real business problems, rapidly building prototypes, and delivering production-ready AI applications. This individual will embed with teams across Finance, Human Resources, Supply Chain, Operations, Sales, Engineering, Advanced R&D, Customer Support, and other functional areas to understand their workflows and implement practical AI solutions that create measurable business value. This position requires a combination of software engineering, product thinking, and strong collaboration skills. The successful candidate will help drive enterprise AI adoption while establishing reusable patterns and best practices that scale across the organization.

Requirements

Bachelor's or Master’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.
0–3 years of professional software engineering or application development experience.
Strong analytical and problem-solving skills.
Ability to manage complexity, take initiative in problem solving, and think critically under pressure.
Experience building applications utilizing Large Language Models (LLMs), AI APIs, AI agents, or similar generative AI technologies through professional work, personal projects, research, or open-source contributions.
Ability to quickly learn new technologies and adapt to evolving AI capabilities.
Excellent communication and interpersonal skills.
Ability to work independently while managing multiple priorities in a fast-paced environment.

Nice To Haves

Experience with OpenAI APIs, ChatGPT Enterprise, Azure OpenAI, Anthropic Claude, Gemini, or similar enterprise AI platforms.
Experience with agent frameworks such as OpenAI Agents SDK, LangGraph, CrewAI, or similar orchestration frameworks.
Familiarity with Retrieval-Augmented Generation (RAG), vector databases, semantic search, and Model Context Protocol (MCP).
Experience with Azure, AWS, or GCP cloud platforms.
Experience with Docker, GitHub, CI/CD pipelines, or Kubernetes.
Experience developing internal productivity tools or workflow automation solutions.
Contributions to open-source projects, hackathons, AI competitions, or personal AI projects that demonstrate technical curiosity and initiative.

Responsibilities

Partner with business stakeholders to understand workflows, pain points, and opportunities for AI-enabled automation.
Design, develop, test, and deploy AI-powered applications, assistants, agents, and workflow automations.
Rapidly build proof-of-concepts and minimum viable products to validate business value.
Iterate quickly based on user feedback and operational needs.
Develop reusable components and implementation patterns that can be leveraged across multiple business functions.
Build full-stack applications using modern software development frameworks and AI technologies.
Integrate AI solutions with enterprise platforms including Microsoft 365, SharePoint, Oracle, Salesforce, Jira, and other internal systems.
Assist with deploying and supporting AI applications in cloud-based environments.
Build solutions utilizing Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and other emerging AI technologies.
Evaluate AI applications for accuracy, reliability, usability, and business effectiveness.
Stay current on advances in AI technologies and identify opportunities to introduce new capabilities into the organization.
Contribute to enterprise AI standards, development practices, and reusable frameworks.
Embed within business functions to understand operational challenges and identify opportunities for AI transformation.
Collaborate with functional areas to ensure solutions align with enterprise architecture and governance standards.
Communicate technical concepts effectively to both technical and non-technical audiences.
Support user adoption through demonstrations, documentation, and knowledge sharing.