Role overview

Senior Backend Engineer – Automation & Platform Implementation

Requirements and responsibilities

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🧱 Platform Development & Backend Architecture

  • Build, extend, and optimize Java-based microservices responsible for task execution, state management, and orchestration logic.
  • Model and manage NoSQL document databases to track execution state, workflow metadata, and session/context persistence.
  • Define internal API contracts, adapters, and connectors that enable modular integration with external APIs and customer deployments.

🧠 AI-Related Backend Engineering

  • Implement production-ready infrastructure for data ingestion, document chunking, embedding indexing, and retrieval (RAG) pipelines.
  • Architect backend services that support multi-step agentic flows, including context assembly, tool calls, and session memory.
  • Turn product requirements for semantic search and AI assistant features into resilient, production-grade backend code.

🤖 Customer Implementation & Enablement (FDE Work)

  • Deploy, configure, and customize the automation platform to meet specific customer use cases and business requirements.
  • Connect external systems through custom sync pipelines, webhooks, and enterprise integrations (e.g., OAuth, ETL-style ingestion).
  • Debug production issues across our stack and customer environments, capturing field insights to improve the core platform.
  • Enhance platform observability by setting up robust tracing, execution visibility, and debugging tools for failed runs.

Core Backend Engineering

  • 4–6 years of software engineering experience building resilient production backend systems and working with distributed architectures.
  • Strong proficiency in Java, with deep experience developing scalable microservices, handling asynchronous jobs, retries, idempotency, and system failure modes.
  • Experience with NoSQL document databases (e.g., MongoDB, Firestore, Couchbase) for state and metadata tracking.

Automation & AI-Aware System Experience

  • Experience building or integrating workflow engines or enterprise automation systems (beyond one-off scripting).
  • Working knowledge of Python for building integrations, tooling, and rapid prototyping.
  • Solid understanding of LLM application structures, including embedding systems, vector search, retrieval mechanics, context windows, and agent/tool execution.
  • Strong interest or hands-on experience in semantic search, RAG pipelines, or conversational AI backends.

Professional Execution & Mindset

  • High Agency & Independent Execution: You have a strong sense of ownership and the exceptional self-direction required to navigate undocumented paths, make sound architectural calls, and take a project to completion with minimal guidance.
  • Thrives in a Startup Environment: You are energized by a fast-paced, evolving startup environment, are comfortable when requirements pivot, and are explicitly not looking for a predictable, mature role at a big company.
  • Wears Multiple Hats Comfortably: You gladly step outside a narrow engineering lane. You can context-switch seamlessly between core backend engineering, customer fire drills, and partner-facing implementation without losing quality.
  • Customer-Facing Technical Experience: Proven ability to interface with product teams, solution architects, and customer stakeholders to translate vague user requirements into working, deployed software.
  • Strong Documentation Culture: A consistent habit of writing clear system designs and deployment guides so that the next team member never has to start from zero.

Nice to haves

  • Familiarity with durable execution systems or workflow engines (e.g., Temporal, Airflow, Camunda).
  • Experience designing plugin, connector, or developer framework ecosystems.
  • Understanding of Edge/serverless computing or Backend-for-Frontend (BFF) patterns for customer-facing apps.
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Browse stack
FocusBackend EngineeringRole area
Seniority signalSeniorCandidate level
StackJava, LLM, MongoDBPrimary skills
Location1 accepted countryEligibility

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