Forward Deployed Engineer – Enterprise AI Solutions Architect

Resilinc

Forward Deployed Engineer responsible for complex enterprise deployments of AI solutions in supply chain management. Collaborating with customers and internal teams to deliver scalable and maintainable solutions.

Posted 5/4/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

CloudERPPySparkPython

About the role

Key responsibilities & impact

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.
Lead complex enterprise deployments from ambiguity to production
Partner directly with strategic customers and internal teams to understand business goals, technical constraints, data realities, delivery risks, and success metrics.
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, and customer-specific data validation and enrichment tools.
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.
Establish strong engineering discipline for everything shipped, including clean code, testing, documentation, security considerations, logging and observability, deployment readiness, operational handoff, and supportability.
Work on the most demanding deployments where standard playbooks are not enough.
Identify repeated patterns across enterprise deployments and feed clear, actionable recommendations back to Product and Engineering.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Full-stack benefits for health, wealth and wellbeing

ATS Keywords

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Hard Skills & Tools

PythonDatabricksPySparkdata pipelinesdata ingestiondata transformationdata validationdata warehouse architecturecloud-native application designproduction-grade workflows

Soft Skills

strong written communicationhigh ownershipstrong executionability to work with technical and non-technical stakeholdersproduct and systems thinkingdisciplined documentationoperating in ambiguous environmentsidentifying repeatable patternscollaborationproblem-solving