- Lead technical discovery and solution design: Collaborate with business and functional stakeholders to understand workflows, challenges, objectives, and constraints, translating them into technical requirements, implementation plans, acceptance criteria, and measurable outcomes.
- Develop AI-powered solutions: Independently design, build, test, and deploy AI agents, automations, dashboards, data pipelines, integrations, and workflow tools that improve productivity and business performance.
- Own the full solution lifecycle: Drive initiatives from discovery, prototyping, and feasibility testing through development, evaluation, deployment, adoption, production support, and ongoing optimization.
- Deliver reliable production software: Write, review, and maintain clean, well-tested, documented code that follows engineering standards and supports long-term reliability, scalability, and maintainability.
- Build secure integrations: Connect business applications, relational databases, systems of record, and internal AI capabilities using API-first approaches, secure authentication, authorization, data validation, logging, and robust error handling.
- Establish reusable technical capabilities: Develop shared libraries, connectors, workflow components, integration patterns, evaluation frameworks, templates, and implementation guides that can be adopted across teams.
- Apply AI tools throughout development: Use AI coding assistants, agents, agent builders, and workflow automation tools to accelerate development, testing, documentation, analysis, and software maintenance while validating outputs before deployment.
- Ensure responsible AI and operational reliability: Implement evaluations, human-in-the-loop controls, privacy safeguards, monitoring, observability, and security measures to ensure AI-enabled workflows operate safely and consistently.
- Manage production systems: Configure environments, monitor system health, troubleshoot incidents, resolve performance issues, and participate in on-call support when required to minimize downtime and business disruption.
- Measure adoption and business impact: Track usage, adoption, time savings, quality, performance, and other agreed metrics, using stakeholder feedback and operational data to prioritize improvements.
- Collaborate across technical and business teams: Work with Product, Engineering, AI and Data, Revenue, Marketing, and other functional stakeholders to distinguish configuration needs from platform gaps and recommend scalable solutions.
- Contribute to shared AI platform development: Dedicate approximately 20% of your time to improving shared AI capabilities through reusable connectors, components, skills, evaluations, guardrails, documentation, and engineering patterns.
- Share knowledge and mentor others: Participate in engineering forums, demonstrate solutions, document lessons learned, contribute to architecture and security standards, and provide technical guidance to junior engineers and business teams.
- Communicate technical decisions effectively: Explain implementation options, trade-offs, risks, dependencies, progress, and outcomes to technical teams, business stakeholders, and executive audiences.
- Maintain high standards of security and professionalism: Follow secure development, data privacy, accessibility, change management, and responsible AI practices while protecting sensitive business, customer, and financial information.
Requirements
- Education and experience: Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience. Typically, at least five years of relevant professional experience is required, or three years combined with an advanced degree.
- Software engineering expertise: Strong programming skills in Python, TypeScript, JavaScript, Java, C#, or a comparable language, with demonstrated experience building, deploying, and supporting production software, data systems, or workflow automation.
- Applied AI experience: Hands-on experience building or integrating AI-enabled workflows, agents, or automations using coding assistants, agent builders, prompt and tool design, evaluations, human review, and safeguards for sensitive data. Regular use of AI development tools and a portfolio of agents or automations are expected; general familiarity alone is insufficient.
- Proven production AI delivery: Experience shipping at least one production AI or agent-based system that has been adopted by another team, remains actively used, delivers measurable value, and has clear ownership and ongoing support.
- Software development lifecycle: In-depth understanding of software design, development, testing, cloud-native deployment, monitoring, and production support, including version control, code reviews, automated testing, and continuous integration and delivery practices.
- Data systems and databases: Experience with SQL and NoSQL databases, data structures, caching, data quality, and diagnosing or optimizing data access and application performance.
- APIs and integrations: Practical knowledge of REST API best practices, webhooks, authentication and authorization protocols, business application integrations, and systems of record.
- Testing and operational support: Experience developing unit, integration, or UI automation tests, or evaluations for AI-enabled workflows, along with familiarity with production monitoring, troubleshooting, performance measurement, and adoption tracking.
- Stakeholder-facing technical delivery: Ability to work directly with business, functional, customer, or partner stakeholders to clarify ambiguous requirements and translate them into effective technical solutions with measurable outcomes.
- Analytical and organizational skills: Strong problem-solving, analytical, and time-management abilities, with the capacity to work independently, manage dependencies, adapt to changing priorities, and deliver results in an iterative environment.
- Communication and collaboration: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to diverse audiences and collaborate effectively across engineering, product, AI and Data, and business teams.
- Security and responsible AI: Understanding of secure development, access controls, data privacy, operational reliability, and responsible AI practices, particularly when handling sensitive customer or financial data.
- Production support flexibility: Willingness to participate in production support activities or an on-call rotation when required.
- Preferred industry experience: Background in SaaS, financial technology, banking, credit unions, or other regulated industries.
- Preferred stakeholder-facing experience: Experience in forward-deployed engineering, consulting, implementation, customer engineering, business systems, revenue operations, marketing operations, or similar technical roles.
- Preferred cloud and infrastructure skills: Experience deploying applications or integrations on Amazon Web Services, using Kubernetes, event-driven services, infrastructure as code, or comparable cloud technologies.
- Additional preferred expertise: Familiarity with modern data warehouses, CRM platforms, sales and marketing systems, customer success workflows, conversation intelligence, observability, OpenTelemetry, performance optimization, or operational reliability practices. Knowledge of the Model Context Protocol (MCP) or comparable AI-to-application integration tools is also an advantage.
Benefits
- Competitive salary: Annual compensation ranging from $128,000 to $160,000.
- Remote-first work environment: The flexibility to work remotely from the United States.
- Unlimited paid time off: A flexible paid leave policy to support personal needs and work-life balance.
- Retirement savings: Access to a 401(k) plan with an employer match.
- Comprehensive benefits: A competitive benefits package designed to support employee well-being and financial security.
- Inclusive workplace culture: A diverse, collaborative environment that values innovation, creativity, and individual contributions.
- Career development: Opportunities to expand technical expertise, contribute to shared AI capabilities, mentor colleagues, and influence engineering practices.
- Meaningful technical impact: The opportunity to build production-grade AI systems and automation solutions that improve productivity and transform business workflows.
- Cross-functional collaboration: Work closely with engineering, product, data, and business teams on challenging initiatives at the intersection of software development and applied AI.
