Senior Go Engineer (GenAI)
Fully remote | Complete engagement job
Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world.
At Factored, you’ll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We’re transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive.
We are looking for a Senior Go Engineer to design and build the backend systems that power AI-native platform workflows at enterprise scale. You will transform complex engineering challenges into secure, robust, production-grade Go services — integrating LLMs, automating quality pipelines, and enabling event-driven architectures used by global organizations. This role exists for engineers who want to own hard problems end-to-end and build at the intersection of Go backend engineering and production AI.
Functional Responsibilities:
- Architect, design, and implement production-grade backend services in Go that integrate with LLMs, ensuring systems are scalable, secure, and reliable.
- Build AI-augmented CI/CD pipelines and automated quality gates, including integration tests, contract tests (OpenAPI and Avro compliance), and pre-merge validation.
- Implement agentic architectures for complex multi-step workflows, tool use, planner/executor patterns, and AI-assisted automation pipelines.
- Design and build event-driven services using Kafka for cross-service data propagation and real-time workflow orchestration.
- Build and maintain contract-first, spec-driven development workflows: schema validation, code scaffold generation, and contract compliance in CI.
- Design knowledge infrastructure for AI tools: structuring organizational context, conventions, and rules so AI systems produce consistent, high-quality, system-specific output.
- Define and enforce quality standards for AI-generated artifacts, ensuring outputs verify real behavior and meet production reliability requirements.
- Monitor system and AI pipeline health using observability tools , logging, metrics, LLM performance tracking , and proactively resolve issues.
- Maintain high engineering standards: modular code, automated testing, thorough documentation, and pragmatic decision-making in greenfield problem spaces.
- Partner with platform and product teams in a collaborative, high-ownership environment, driving architecture decisions and implementations end-to-end.
Qualifications:
- 5–7 years of professional experience as a Software Engineer, with strong Go (Golang) proficiency as the primary language.
- Hands-on production experience with AI/LLM integration, shipped systems with real usage, not experimentation or POCs; daily use of AI coding assistants (Copilot, Cursor, Claude Code, or equivalent).
- Experience building or working with contract-first development workflows (OpenAPI, Avro, or schema-driven); Experience with AI-augmented CI/CD pipelines, automated quality gates is a plus.
- Experience with event-driven architectures and Kafka for cross-service data propagation; familiarity with Avro schemas and message contract design.
- Practical experience with AWS, including EKS and container-based deployments (ECS, Lambda, or equivalent cloud-native infrastructure).
- Experience designing systems for multi-tenant or hierarchically structured data models, and working with PostgreSQL in production environments.
- Strong system design and problem-solving skills, including scoping ambiguous problems, evaluating LLM vs. deterministic approaches, and knowing when not to use AI.
- Experience with structured data extraction from unstructured inputs using LLMs, or knowledge engineering for AI tools (structuring context and instructions for consistent AI output) is a plus.
- A pragmatic mindset: you value pragmatic delivery over over-engineered solutions and bring measurable outcomes to back your decisions.
- Excellent English communication skills, both written and spoken, with the ability to collaborate across distributed teams and articulate complex technical decisions clearly.
- Familiarity with workflow orchestration tools such as Temporal or Airflow; experience with Kubernetes (EKS) deployment and operations is a plus.
- Exposure to knowledge graphs, ontologies, or graph-based data modeling; experience with SCIM, SAML, or enterprise identity provisioning patterns is a plus.
Our Benefits:
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance culture.
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