Accountabilities

Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.

Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.

Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.

Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.

Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.

Develop and maintain comprehensive evaluation frameworks and automated tests based on real-world customer use cases to measure agent performance and prevent regressions.

Define architectural standards, engineering patterns, and best practices for building, testing, deploying, and maintaining agentic systems across the engineering organization.

Stay ahead of developments in AI models, agentic frameworks, orchestration tools, and emerging technologies, incorporating valuable innovations into the product architecture.

Rapidly prototype new AI capabilities and present technical concepts and product opportunities to internal teams and customers.

Work hands-on with AI-assisted development tools such as Claude Code, continuously improving personal engineering workflows and sharing effective practices with the wider team.

Collaborate closely with engineering leadership, product teams, and customers to shape the AI roadmap, prioritize initiatives, and translate business challenges into impactful technical solutions.

Provide technical mentorship and influence engineering direction while maintaining a strong focus on execution, product quality, and measurable customer outcomes.

Requirements

At least 7 years of full-stack software development experience, ideally building and scaling B2B SaaS applications.

Proven, hands-on experience designing and deploying production-grade multi-agent systems, beyond experimental prototypes or personal projects.

Demonstrated experience building and maintaining MCP servers or integrations used in production environments.

Strong understanding of production engineering principles, including system reliability, observability, security, permissions, and safe workflow execution.

Experience combining product thinking with technical leadership, including defining scope, making architectural decisions, and influencing product roadmaps rather than focusing exclusively on implementation.

Proficiency in React, TypeScript, Node.js, PostgreSQL, GraphQL, and LangGraph, with the ability to quickly learn and adopt new technologies and frameworks.

Previous experience in an early-stage startup environment, preferably within an engineering team of fewer than 20 people, with a track record of taking ownership and setting direction in ambiguous situations.

Strong practical AI fluency, including daily use of Claude Code or comparable AI development tools, and the ability to build custom agents, skills, and automated workflows.

Ability to design and implement effective AI evaluation processes, understand when to use language models versus conventional code, and build appropriate safeguards around AI-generated actions.

Experience deploying AI agents to real users with suitable guardrails, monitoring, observability, and rollback mechanisms.

Strong product and engineering judgment, with the ability to distinguish high-value opportunities from unnecessary complexity and deliver intuitive AI-powered experiences.

Excellent communication skills, with the confidence to raise concerns, propose solutions proactively, and communicate complex technical concepts clearly.

Collaborative, low-ego mindset, prioritizing team success, product quality, and shipped outcomes over internal politics or individual recognition.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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