Forward Deployed Engineer (Enterprise AI Solutions Architect) – US (Remote)

Deliver production-ready technical solutions for Resilinc’s most complex enterprise customers by turning ambiguous business, data, workflow, and AI deployment requirements into scalable, well-documented, supportable software and implementation patterns.

Partner directly with strategic customers and internal teams to understand business goals, technical constraints, data realities, delivery risks, and success metrics. Translate those inputs into a clear technical approach that accelerates deployment and value realization.

Design, build, and deliver production-quality solutions required for complex deployments, including:

• Data ingestion and transformation utilities
• ERP, Snowflake, Databricks, API, and external system integrations
• Workflow automations
• Agentic AI deployment extensions
• Customer-specific data validation and enrichment tools
• Lightweight services or applications that support go-live readiness
• Repeatable accelerators for future enterprise deployments

Help customers deploy and operationalize Resilinc’s agentic capabilities across use cases such as disruption intelligence, tariff risk, forced labor compliance, supplier risk, multi-tier mapping, and event-driven workflows.

You will help bridge the gap between the platform’s agentic capabilities and each customer’s real-world data, process, governance, and operating model.

Establish strong engineering discipline for everything you ship, including:

• Clean code
• Testing
• Documentation
• Security considerations
• Logging and observability
• Deployment readiness
• Operational handoff
• Supportability

The goal is not just to solve the immediate customer problem. The goal is to solve it in a way that another team can understand, operate, and improve.

Work on the most demanding deployments where standard playbooks are not enough. Help unblock go-lives, stabilize early production usage, reduce escalation risk, and accelerate measurable customer outcomes.

Identify repeated patterns across enterprise deployments. Distinguish true one-off customization from broader market needs. Feed clear, actionable recommendations back to Product and Engineering so Resilinc can convert repeated customer needs into reusable platform capabilities.

Contribute to the operating model, standards, tooling, reusable templates, engagement rules, and handoff practices for this new function. The goal is to create leverage, not heroics.

This role is designed to solve hard technical deployment problems and create reusable patterns.

• Complex deployment engineering
• Customer-specific technical solution design
• Production-ready accelerators
• Integration and workflow problem solving
• Agentic AI deployment enablement
• Technical unblockers for strategic accounts
• Reusable implementation patterns
• Product feedback from real enterprise deployments

• Routine customer support
• Long-term maintenance of every custom solution
• Standard implementation project management
• Core product roadmap ownership
• General pre-sales demonstrations
• One-off hero work without documentation or handoff

Success in this role will be measured by your ability to:

• Reduce time-to-value for complex enterprise deployments
• Unblock strategic customer go-lives
• Improve deployment quality and predictability
• Reduce long-term operational debt from custom work
• Create reusable accelerators, templates, and implementation patterns
• Improve handoff quality to Agent Success, Support, and Engineering
• Surface product gaps and repeated customer patterns with clear recommendations
• Help customers operationalize Resilinc’s agentic AI capabilities in production

• 6+ years of experience in software engineering, implementation engineering, applied AI engineering, solutions engineering, data engineering, or a similarly technical role in enterprise software or AI-first SaaS
• Strong hands-on coding experience, especially in Python
• Experience building and shipping production-grade workflows, services, applications, integrations, or data utilities
• Hands-on experience with Databricks, including PySpark, Delta tables, pipeline development, and data-sharing patterns
• Experience building and managing data pipelines that ingest, validate, transform, and operationalize data from sources such as Snowflake, ERP systems, APIs, flat files, and customer data warehouses
• Experience designing or contributing to data warehouse architecture, scalable ingestion workflows, data models, and transformation logic
• Strong understanding of cloud-native application design and production practices, including deployment, testing, logging, monitoring, observability, and operational support
• Experience translating ambiguous customer requirements into clear technical solutions, delivery plans, and production-ready outputs
• Ability to work effectively with technical and non-technical stakeholders, including executives, supply chain leaders, IT teams, data teams, implementation teams, Product, Engineering, Agent Success, and Support
• Strong written communication skills, with disciplined documentation and handoff practices
• High ownership, strong execution, and comfort operating in ambiguous, fast-moving enterprise environments
• Strong product and systems thinking, including the ability to identify repeatable patterns and separate one-off customization from broader platform opportunities