Sr. Forward Deployed Engineer

Alkami Technology•Bentonville, AR

1d•$128,000 - $160,000•Remote

About The Position

The Sr. Forward Deployed Engineer independently drives delivery of internal AI, automation, data, and workflow solutions for Alkami’s business and functional teams. This hands-on individual contributor translates complex business needs into technical designs, production software, AI-enabled workflows, integrations, dashboards, and reusable capabilities. The role participates in solution design from beginning to end, applies deep technical and business domain knowledge, and partners with Revenue, Marketing, Product, Engineering, AI and Data, and other stakeholders. The engineer owns moderately complex initiatives through discovery, design, build, testing, deployment, adoption, and continuous improvement; contributes to roadmaps and technical standards; supports production systems; and helps develop other team members. Approximately 20% of the role is dedicated to improving Alkami’s shared AI platform through reusable connectors, components, skills, evaluations, guardrails, documentation, and patterns. The role also participates in production support or on-call response when required. Alkami is a remote-first company, and this position is described as remote-eligible in the United States. The salary range is $128,000–$160,000. Alkami cannot provide employment sponsorship; candidates must be eligible to work in the United States for full-time employment.

Requirements

At least 5 years of related experience in software development, technical implementation, data systems, automation, or a related field; alternatively, 3 years of experience and an advanced degree.
Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
Strong programming skills in Python, TypeScript, JavaScript, Java, C#, or a comparable language, with experience shipping and supporting production software, data systems, or workflow automation.
Experience working directly with business, functional, customer, or partner stakeholders to translate ambiguous needs into technical approaches and measurable outcomes.
Experience building or integrating AI-enabled workflows, agents, or automations using coding assistants, agent or workflow builders, prompt and tool design, evaluations, human review, and sensitive-data safeguards.
Ability to apply AI-assisted workflows to code generation, testing, documentation, and refactoring while validating outputs.
Demonstrated daily use of AI tools and a portfolio of agents or automations; general familiarity with AI tools is not sufficient.
At least one production AI or agent-based system shipped, adopted by another team, actively used, measurably valuable, clearly owned, and supported on an ongoing basis.
In-depth understanding of the software development lifecycle, including design, development, testing, cloud-native deployment, monitoring, and production support.
Working knowledge of version control, code review, automated testing, and CI/CD practices.
Experience with SQL and NoSQL databases, data structures, caching mechanisms, data quality, and troubleshooting or optimizing data access and application performance.
Understanding and practical application of REST API best practices, with experience integrating APIs, webhooks, authentication and authorization protocols, business applications, and systems of record.
Experience developing unit tests, UI automation tests, integration tests, or evaluations for AI-enabled workflows.
Familiarity with monitoring and supporting production systems, identifying and resolving issues, and tracking performance or adoption measurements.
Ability to collaborate effectively within an agile or iterative framework, with a problem-solving attitude and willingness to take varied approaches.
Strong analytical and time-management skills, including the ability to deliver value independently, manage dependencies, and adapt to changing priorities.
Strong written and verbal communication skills, including explaining complex concepts to technical and nontechnical audiences and working cross-functionally.
Ability to participate in production support or an on-call rotation when required by the supported solution.
Understanding of secure development, access controls, data privacy, operational reliability, and responsible AI practices when working with company, customer, and financial data.

Nice To Haves

Experience working with SaaS offerings in technology and financial industries, including banking, credit unions, fintech, or another regulated environment.
Experience in a forward-deployed, consulting, implementation, customer engineering, business systems, revenue operations, marketing operations, or other stakeholder-facing technical role.
Experience building and deploying applications or integrations on Amazon Web Services using Kubernetes, event-driven services, infrastructure as code, or comparable cloud technologies.
Experience with modern data warehouses, CRM, customer success, marketing, sales, or conversation-intelligence data and workflows.
Experience in observability, OpenTelemetry, performance optimization, or operational reliability practices.
Working knowledge of MCP or comparable tools for connecting AI systems to business applications, or experience contributing to an internal AI platform, shared engineering foundation, or community of practice.

Responsibilities

Lead discovery with business and functional stakeholders, translating workflows, goals, constraints, and pain points into requirements, technical designs, implementation plans, acceptance criteria, and measurable outcomes.
Independently design, build, test, and deploy moderately complex AI agents, skills, dashboards, automations, data pipelines, and integrations.
Participate in the full solution lifecycle, from prototyping and feasibility testing through production implementation, adoption, support, and continuous improvement.
Write, review, and maintain clean production code with automated tests, useful documentation, clear ownership, and alignment to team development standards.
Connect business applications, systems of record, relational databases, and internal AI capabilities using secure, maintainable, API-first integration approaches.
Map data flows and implement authentication, authorization, validation, logging, error handling, monitoring, and data provenance for integrations and AI-enabled workflows.
Build and maintain reusable code, libraries, workflow components, integration patterns, evaluation approaches, and implementation guides.
Own production readiness and support, including testing, evaluations, security, data validation, observability, error handling, human-in-the-loop controls, and production support.
Use AI tools, coding agents, agent builders, and workflow automation in building, testing, documenting, analyzing, and operating software.
Partner with Product, Engineering, AI and Data, and functional teams to distinguish configuration needs from product or platform gaps and recommend practical, scalable solutions.
Design and execute evaluations, human-in-the-loop controls, and responsible AI safeguards; validate AI-assisted work before business use.
Configure and maintain environments, monitor system health, investigate production issues, and improve reliability and performance.
Track adoption, usage, hours saved, quality, and other agreed outcomes; use stakeholder feedback and operational data to prioritize improvements.
Contribute to the canonical set of approved skills, agents, dashboards, templates, and documentation for the supported function; retire or consolidate duplicative solutions when appropriate.
Participate in the weekly FDE forum by sharing demos, lessons learned, reusable patterns, technical challenges, and business outcomes, and contribute to shared architecture, security, evaluation, and responsible AI standards.
Provide training, technical support, and mentoring to junior engineers, business builders, and functional partners.
Communicate technical options, tradeoffs, risks, dependencies, progress, and decisions clearly to technical, nontechnical, and executive stakeholders.
Spend approximately 20% of time improving Alkami’s shared AI platform with reusable connectors, components, skills, evaluations, guardrails, documentation, and patterns.
Respond promptly to production incidents or on-call issues, troubleshooting and resolving critical disruptions to minimize downtime and business impact.
Apply security, privacy, accessibility, change-management, and responsible AI standards, and protect company, customer, and financial data.
Adhere to and uphold Alkami’s Values.