Forward Deployed Solution Engineer – Applied AI FDE

Role and team

What you’ll do

• Discover customer problems, lead workshops, and frame high-impact AI opportunities.
• Design, build, test, and deploy GenAI/ML systems, including RAG, agentic workflows, evaluation, and guardrails.
• Prototype with users and harden pilots into reliable, monitored services.
• Translate between customer business stakeholders and engineering; align customers and executives through clear solution framing.
• Create reusable frameworks, templates, modular components, shared tools, and SDK contributions.
• Capture deployment insights for product strategy and platform priorities; provide documentation and technical onboarding to support scale.

Required qualifications

• 8+ years of software engineering, including 2+ years building and shipping systems in customer-facing or embedded roles.
• 3+ years building production ML/AI systems end to end, including data preparation, deployment, monitoring, and iteration.
• Hands-on LLM application experience with RAG, embeddings, vector databases, prompt engineering, structured outputs, tool calling, context management, and agentic workflows.
• Experience with system evaluation, metrics, test harnesses, production diagnostics, and ML foundations.
• Experience with relevant AI frameworks, ML libraries, Hugging Face, and major model platforms or APIs.
• Strong backend, frontend, API, distributed systems, performance, and observability skills.
• Cloud deployment experience on AWS, Azure, or GCP with CI/CD, containers, and infrastructure as code.
• Practical responsible AI experience, including guardrails, PII handling, prompt-injection mitigation, output validation, and data governance.
• Able to travel up to 30% for onsite customer work.

Preferred qualifications

• Experience integrating AI into SaaS platforms such as ServiceNow or Salesforce.
• Fine-tuning and adaptation experience, including LoRA/PEFT, distillation, or quantization.
• Inference optimization, self-hosted model serving, and GPU cost/performance tuning.
• ML data engineering experience with pipelines, feature stores, streaming ingestion, or large-scale document processing.
• Experience delivering AI systems in regulated or data-sensitive environments.
• Comfort leading customer technical workshops, discovery sessions, and architecture reviews.

Success measures

Work arrangement

• Travel up to 30% is expected for onsite customer embedding and delivery.
• The employer describes flexible, remote, and required-in-office work personas but does not specify which applies to this role.