Forward Deployment Engineer
$117,800–$212,500 year
On-siteAtlanta, Georgia, United States or Bellevue, Washington, United States
Job Summary
Implement and iteratively tune net-new agentic AI solutions in assigned business domains, adapting prompt layers and escalation logic based on weekly operational feedback. Run controlled A/B experiments in live production cohorts to validate return on investment targets, feeding results to leadership for delivery prioritization. Build system integrations connecting AI systems to enterprise data sources like CRM, billing, and ERP platforms, including lightweight ETL pipelines for evaluation loops. Embed directly with business unit teams across care, retail, finance, or supply chain to identify workflow friction and conduct structured feedback sessions. Document failure modes, edge cases, and performance gaps to support domain planning and practice reviews.
Required Qualifications
- Bachelor's Degree Computer Science, Software Engineering, Data Science, or related technical field
- 2-4+ years hands-on software or ML engineering experience in production environments
- Demonstrated ability to write clean, maintainable Python code and to debug and extend existing codebases
- Experience deploying to cloud environments and building system integrations with enterprise data sources
- 4-7+ years prior experience in a customer-facing, operations, or embedded technical role (care, retail, finance, supply chain)
- At least 18 years of age
- Legally authorized to work in the United States
- Travel Required (Yes/No): Yes
Desired Qualifications
- Exposure to enterprise AI platforms such as Salesforce Einstein, ServiceNow AI, or Microsoft Copilot
- Python proficiency in production settings: clean, maintainable code; able to read, debug, and extend existing agentic system code written by others
- Working knowledge of LLM integration patterns: prompt engineering, RAG basics, function calling; experience with at least one major LLM provider
- SQL proficiency; experience building system integrations connecting AI solutions to enterprise data sources (CRM, ticketing, ERP, billing); able to build lightweight ETL/ELT pipelines and instrument systems with basic logging
- Comfortable deploying to AWS or Azure, using Docker, and reading CI/CD pipelines
- Ships iterative improvements in days, not weeks; able to communicate technical trade-offs clearly to non-technical partners
- Solid understanding of data privacy requirements in enterprise environments including PII and customer data protection
- Applies responsible AI principles and appropriate data handling practices when building system integrations and AI solutions
- Demonstrated ability to embed within a business unit, earn stakeholder trust rapidly, and deliver working AI systems under real operational constraints, not in sandbox or lab environments
- Ability to move fluidly between technical implementation and business communication, converting engineering tradeoffs into plain-language impact narratives for BU managers, and converting vague business asks into precise engineering requirements
- Experience compressing the gap between proof-of-concept and production-grade delivery
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