Staff AI Forward Deployed Engineer

About the Role

A large, fast-moving enterprise organization is seeking a Staff AI Forward Deployed Engineer to help accelerate practical AI adoption across the business.

This is a highly visible, hands-on role within an Enterprise Software Infrastructure organization. The engineer will serve as a go-to resource for evaluating AI opportunities, translating business needs into technical solutions, building prototypes and integrations, and coordinating delivery across business, product, engineering, infrastructure, data, governance, and executive teams.

*This is not an AI research, machine learning research, or foundation model development position.*

The ideal candidate combines strong software engineering and applied AI experience with exceptional communication, business judgment, and stakeholder-management skills. This person must be comfortable working directly with Directors, Vice Presidents, and C-level executives while remaining hands-on with coding, integrations, data, and AI tooling.

The role is approximately:

• 50% hands-on engineering, coding, integrations, data aggregation, and prototyping
• 50% stakeholder discovery, cross-functional leadership, prioritization, governance, and executive communication

What You Will Do: AI Solutioning and Engineering

• Meet directly with business and executive stakeholders to understand their workflows, challenges, data, and desired outcomes.
• Analyze ambiguous requests and identify the actual business problem behind the initial ask.
• Separate meaningful information from noise and determine which details are relevant to the solution.
• Evaluate whether AI is the right approach or whether a simpler technical solution would be more effective.
• Translate business problems into clear technical requirements, implementation plans, and measurable outcomes.
• Research emerging AI capabilities and determine how they can be applied safely and practically within an enterprise environment.
• Design and build AI-enabled workflows, internal applications, prototypes, and automation.
• Develop retrieval-augmented generation solutions using approved internal company data.
• Build LLM, API, CRM, ERP, cloud, and enterprise-data integrations.
• Pull and aggregate information across systems such as Salesforce, Snowflake, AWS, GCP, internal APIs, and enterprise applications.
• Build backend services, APIs, data workflows, automation, and lightweight user interfaces.
• Create solutions that answer business questions using trusted enterprise data.
• Evaluate feasibility, dependencies, delivery timelines, risks, access requirements, and operational impact.
• Determine when a prototype has become operationally important and should be transitioned to a specialized production engineering team.
• Remain the primary business and technical point of contact through delivery, adoption, iteration, and handoff.

Executive and Stakeholder Partnership

• Communicate comfortably with Directors, Vice Presidents, and C-level executives.
• Provide concise executive summaries, project updates, recommendations, and decision documents.
• Translate complex technical concepts into clear business language.
• Adjust communication based on the audience, including executive leadership, product teams, and engineering teams.
• Explain the same technical project differently to a CTO, COO, and software engineering team.
• Know when to provide technical depth and when to remain focused on business impact, decisions, and risks.
• Present and defend recommendations while respectfully challenging assumptions when appropriate.
• Influence stakeholders and drive alignment without direct authority.
• Gather requirements and coordinate across Engineering, Product, CRM, ERP, Infrastructure, Data, Security, and AI Governance teams.
• Follow up consistently with stakeholders and maintain ownership of ambiguous initiatives.
• Help prioritize AI opportunities based on business value, feasibility, organizational maturity, cost, and risk.
• Develop clear written communication, including design documents, executive updates, project summaries, and implementation recommendations.

Example Projects

Projects may include:

• Combining information from Salesforce, Snowflake, AWS, GCP, and internal systems to answer business questions.
• Building AI-enabled workflows that automate repetitive enterprise processes.
• Creating internal AI tools that retrieve and summarize approved company information.
• Developing retrieval-augmented generation applications using internal documents, data, and business systems.
• Integrating AI tools into CRM and ERP workflows.
• Building prototypes to determine whether a proposed AI use case is technically and operationally viable.
• Creating AI-powered reporting, dashboarding, summarization, document-generation, and decision-support tools.
• Connecting multiple enterprise data sources and identifying the appropriate source-of-truth information.
• Helping business teams adopt approved AI tools and measure their usage and value.

Required Qualifications

• 5 or more years of experience in software engineering, platform engineering, full-stack engineering, applied AI, enterprise integrations, DevOps, or a related technical field.
• Recent hands-on experience building applied AI applications, AI-enabled workflows, internal AI tools, or LLM integrations.
• Strong programming skills in Python, TypeScript, JavaScript, Go, or a comparable language.
• Experience building production-quality backend services, APIs, integrations, automation, or internal enterprise tools.
• Experience working with enterprise data and business systems, including several of the following:
• Salesforce or other CRM platforms
• ERP systems
• Snowflake
• AWS
• GCP
• Internal APIs
• Enterprise data platforms
• Experience connecting multiple systems and identifying reliable source-of-truth data.
• Working knowledge of retrieval-augmented generation, LLM integration, prompt engineering, evaluations, and AI guardrails.
• Experience leveraging existing AI platforms, model providers, or enterprise AI frameworks.
• Ability to evaluate an ambiguous business request and independently develop a practical technical solution.
• Strong executive presence and the ability to work directly with senior business and technical leaders.
• Excellent verbal and written communication skills.
• Ability to explain complex technical projects clearly and concisely.
• Ability to adjust communication style and level of detail for different audiences.
• Strong collaboration, follow-through, and stakeholder-management skills.
• Ability to influence decisions and drive alignment without direct authority.
• Comfort working in a fast-moving, agile environment where priorities and requirements may evolve.
• Bachelor’s degree in computer science, engineering, or a related field, or equivalent professional experience.

Preferred Qualifications

• Previous experience as a Forward Deployed Engineer, Applied AI Engineer, AI Solutions Engineer, Platform Engineer, Full-Stack Engineer, Solutions Architect, or technical consultant.
• Experience working in a startup, high-growth organization, or highly agile engineering environment.
• Experience working across both startup and large-enterprise environments.
• Experience building internal AI tools, developer platforms, workflow automation, or enterprise applications.
• Experience with Google Gemini, Vertex AI, Claude, Microsoft Copilot, or comparable AI platforms.
• Experience with Model Context Protocol, agent frameworks, Text-to-SQL, multi-agent systems, or multi-step AI workflows.
• Experience with React, TypeScript, or other front-end technologies used to build lightweight interfaces.
• Experience working with AI governance, security, data classification, least-privilege access, audit, or compliance teams.
• Experience presenting technical recommendations and project updates to executive leadership.
• Experience evaluating AI solution costs, usage, and operational sustainability.

What Success Looks Like

A successful candidate will be able to:

• Listen to a broad or unclear request and identify the underlying business need.
• Determine what information matters and what can be excluded.
• Decide whether AI is the appropriate solution.
• Translate the request into a realistic technical approach.
• Build a working prototype while coordinating with the necessary technical and business teams.
• Provide meaningful delivery timelines and communicate dependencies and risks.
• Explain technical work clearly without overwhelming stakeholders with unnecessary detail.
• Communicate the same project effectively to executive, operational, and engineering audiences.
• Earn trust as a practical AI advisor and hands-on technical partner.
• Help the organization adopt AI solutions that are useful, secure, measurable, and supportable.

Ideal Candidate

The ideal candidate is a hands-on engineer who is equally comfortable writing code and communicating with senior executives.

They are concise, curious, practical, and comfortable operating in ambiguity. They do not simply build exactly what has been requested. They ask thoughtful questions, uncover the real need, evaluate the organization’s current AI maturity, and recommend a solution that can realistically be adopted and supported.

They can build quickly, collaborate across multiple teams, navigate competing viewpoints, and communicate clear recommendations without getting lost in unnecessary technical detail.

Candidates with experience in startup or high-growth environments are especially relevant because this role requires speed, adaptability, ownership, and the ability to move forward without perfectly defined requirements.

This position is not intended for candidates focused primarily on machine-learning research, statistical modeling, data science, or training foundation models.

Compensation

The position may also be eligible for an annual performance bonus and equity compensation.

Actual compensation will be determined based on the candidate’s experience, technical capabilities, executive communication skills, and ability to operate across applied AI, enterprise systems, and cross-functional stakeholder environments. Both base salary and total compensation are negotiable.

Pay: $160,000.00 - $235,000.00 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Retirement plan
• Vision insurance

Application Question(s):

• Please provide your LinkedIn profile URL (it's required):

Education:

• Bachelor's (Required)

Location:

• United States (Required)

Work Location: Remote