Forward Deployed Engineer

Description

The company is hiring a Forward Deployed Engineer and AI Solution Architect to speed up the design, rollout and scaling of AI-enabled solutions across the enterprise.

The role combines business strategy, enterprise architecture, AI engineering and product delivery to turn high-value business needs into secure, scalable, reusable technical solutions.

You will work with business teams, IT, data owners, security, enterprise architecture, platform owners and AI governance groups to find use cases, shape solution approaches, build prototypes, guide production delivery and set up repeatable patterns across the company.

This is a hands-on role, not advisory only. You will take unclear problems to working solutions by scoping opportunities, building or guiding prototypes, making practical architecture trade-offs, and designing for adoption, maintainability, compliance and scale.

Many valuable AI, generative AI, automation, agent and data use cases stall between problem definition and execution. You will help teams take ideas and pilots to production in line with the company's architecture, data, security, operating model and long-term technology strategy, so that solutions are governed, reusable and measurable rather than one-off experiments.

Success looks like production deployments with measurable adoption and impact, faster and better scoping, prototyping and delivery, more reuse of patterns and playbooks, fewer duplicate pilots, closer alignment between business, architecture, data ownership, governance and platform strategy, and stakeholders who trust the company to deploy AI safely at scale.

Example work includes designing a commercial AI assistant that draws only on approved customer, product and sales content, moving a manual document review process to an AI workflow with human review, auditability and measured productivity gains, building a reusable pattern for GPTs or agents that reach internal knowledge while respecting data permissions, working with data owners on what data a priority use case needs and how it is accessed and maintained, deciding whether a request suits ChatGPT Enterprise, an API-based app, Microsoft Copilot, automation tooling or another platform, and prototyping with business users before handing the work to engineering to harden for production.

Responsibilities

• Work with the AI transformation team and business stakeholders to understand workflows, pain points, decision steps, data needs and the outcomes that can be measured.
• Turn business needs into technical opportunities, solution hypotheses, architecture options and delivery plans.
• Judge each use case on feasibility, business value, data readiness, integration complexity, risk, scalability and fit with the company's AI strategy.
• Agree with AI transformation and business stakeholders on success metrics such as adoption, productivity, workflow improvement, quality, cycle time, cost or risk reduction, and user experience.
• Rank AI opportunities by value, complexity, reusability and enterprise fit.
• Design scalable AI architectures that fit the company's enterprise architecture, security standards, data governance, integration patterns and platform strategy.
• Produce end-to-end technical designs covering user experience, model or platform choice, data access, APIs, orchestration, integrations, security controls, human review steps, monitoring and support.
• Design solutions so other teams, functions and business groups can reuse them where practical.
• Work with enterprise architecture, cybersecurity, data architecture, infrastructure, cloud and application teams to keep solutions within the company's technical ecosystem.
• Decide when to use an existing enterprise platform, extend current capabilities, or build a new pattern.
• Lead or contribute directly to rapid prototypes, proofs of concept and minimum viable solutions that show business value.
• Work hands-on with AI tools, APIs, automation platforms, enterprise systems and data sources to test solution approaches.
• Help engineering and delivery teams take successful prototypes into production.
• Guide teams through trade-offs between speed, scalability, cost, reliability, user experience and governance.
• Make sure production solutions come with documentation, monitoring, clear ownership, a support model, risk controls and a measurement approach.
• Support the design and rollout of AI agents, GPTs, copilots, workflow assistants, automation patterns and AI-enabled applications.
• Define reusable patterns for common solution types such as knowledge assistants, document processing, workflow automation, decision support, data analysis, software development support and agent-driven process execution.
• Advise teams on prompt design, retrieval-augmented generation, model selection, evaluation, grounding, workflow orchestration and responsible use.
• Work with AI governance and platform teams so solutions meet compliance, security and enterprise AI standards.
• Write practical guides, templates and playbooks that help teams build AI solutions consistently and responsibly.
• Turn successful approaches into reference architectures, design patterns, components, templates, playbooks and implementation guidance.
• Find similar use cases that can share a platform or enterprise capability, and connect teams working on similar problems to cut duplication.
• Record lessons from pilots and deployments and feed them back into solution design, platform strategy, governance, data strategy and product roadmaps.
• Deliver reference architectures for priority AI and generative AI patterns, technical scoping documents, solution blueprints covering architecture, data flows, integrations, risks and implementation, working prototypes, and production handoff documents for engineering, support, governance and business owners.
• Provide recommendations on platform, vendor, data and architecture needs based on field experience, and define how adoption, value and solution performance will be measured.

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science or a related technical field, or equivalent practical experience.
• At least 5 years in software engineering, solution architecture, technical deployment, enterprise architecture, AI engineering, data engineering or a similar technical role.
• Experience designing and delivering complex technical solutions in large enterprises.
• Hands-on experience with AI, generative AI, machine learning, automation, API integrations or modern application development.
• Able to write, review or guide production-quality code in languages such as Python, JavaScript, TypeScript, Java or similar.
• Experience with cloud architecture, APIs, identity and access management, data integration, application integration and enterprise security.
• Strong understanding of how large language models, AI agents, retrieval-augmented generation, prompt engineering, evaluation and model behavior shape solution design and user experience.
• Proven ability to work with business stakeholders to understand workflows, define requirements and turn needs into technical solutions.
• Clear communicator who can explain complex ideas to both technical and non-technical audiences.
• Able to work through ambiguity, make sound trade-offs and carry work from concept to delivery.
• Preferred: experience deploying AI or generative AI solutions in a regulated, global or highly matrixed enterprise.
• Preferred: experience with ChatGPT Enterprise, OpenAI APIs, Azure OpenAI, Microsoft Copilot, agent frameworks or similar AI platforms.
• Preferred: experience with enterprise data platforms, knowledge management systems, document repositories, CRM, ERP, commercial platforms, laboratory systems, supply chain platforms or other large business systems.
• Preferred: understanding of data governance, privacy, cybersecurity, regulatory compliance and responsible AI.
• Preferred: experience creating reusable architecture patterns, technical playbooks or internal developer enablement materials.
• Preferred: experience in life sciences, healthcare, diagnostics, manufacturing, commercial operations or scientific and technical domains.
• Preferred: familiarity with the company's internal technology landscape, operating model, business groups, governance processes and architecture standards.
• Critical capabilities: technical depth across applications, data, cloud, security and integration layers; judgment on whether a solution should be a local prototype, a reusable pattern, a platform capability or an enterprise product; connecting AI to real workflows, value drivers and measurable outcomes; building prototypes and validating assumptions; designing secure, compliant, supportable and explainable solutions aligned with responsible AI; and spotting repeatable patterns and building reusable assets.

Conditions

Standard Monday to Friday schedule.

Office environment.

Location is Austin, TX, remote.

Relocation assistance is not provided.

You must be legally authorized to work in the United States. Sponsorship is not available.

Benefits

• Competitive pay.
• Annual incentive plan bonus.
• Healthcare coverage.
• Company 401k.
• A range of other employee benefits.