About the role

This role owns the design and implementation of AI agents that power intelligent workflows across our platform, from reasoning and automation to evidence-driven RCA, customer-facing copilots, internal operations, and product intelligence.

You’re a strong Python engineer who understands modern agentic AI architecture and can turn ambiguous product needs into production-grade systems. You are comfortable building agents that use tools, retrieve context, reason over structured and unstructured data, execute workflows, and integrate deeply with backend services.

This is not a prompt-only role. We’re looking for someone who can architect, build, test, deploy, and operate AI-powered features end to end.

What you’ll build

Agentic AI systems

• Production-grade AI agents for different product and internal applications:
  
  • site-level connectivity analysis
  • evidence-first root cause analysis workflows
  • customer-facing assistant and copilot experiences
  • internal automation for operations, support, and engineering workflows
  • reasoning workflows over telemetry, incidents, site data, and knowledge sources
• Modern agentic architecture:
  
  • tool/function calling
  • planning and execution loops
  • state and memory management
  • retrieval-augmented generation
  • structured outputs
  • workflow orchestration
  • multi-agent patterns where appropriate
  • guardrails, permissions, and safety constraints
  • observability and evaluation frameworks
• AI-native product primitives:
  
  • agent task models
  • context assembly pipelines
  • tool registries
  • prompt/version management
  • human-in-the-loop review flows
  • agent execution traces
  • eval datasets and regression testing

Python backend + product engineering

• Backend services and APIs in Python:
  
  • FastAPI/Django/Flask-style services
  • workers and async jobs
  • integrations with internal systems and external APIs
  • data models supporting AI workflows
  • event-driven and workflow-driven architectures
• Retrieval and knowledge systems:
  
  • vector search
  • hybrid search
  • document ingestion
  • chunking and indexing strategies
  • metadata filtering
  • grounding and citation workflows
• Production AI infrastructure:
  
  • LLM provider integration
  • model routing
  • cost and latency optimization
  • Caching
  • rate limits and retries
  • monitoring and debugging
  • failure handling and fallback behavior
• Feature delivery end to end:
  
  • product scoping
  • Architecture
  • Implementation
  • Testing
  • Deployment
  • Observability
  • iteration based on user feedback

Responsibilities

• Architect and implement production-grade AI agents that solve real business and product problems.
• Build agent workflows that can reason over Eino’s data, tools, telemetry, site models, incidents, and knowledge sources.
• Own agent architecture patterns across planning, memory, retrieval, tool execution, structured outputs, evals, and observability.
• Build and operate core Python backend services that support AI-powered product features.
• Work closely with product, engineering, and leadership to identify high-value agentic AI use cases.
• Move quickly from prototype to production while maintaining reliability, security, and maintainability.
• Establish testing and evaluation discipline for AI systems:
  
  • unit/integration tests
  • prompt and workflow regression tests
  • agent evals
  • golden datasets
  • trace review
  • failure analysis
• Drive practical AI engineering standards:
  
  • correctness over demos
  • grounded outputs
  • measurable quality
  • clear contracts between agents, tools, and backend services

Required qualifications

• Strong experience building production Python systems, including services, APIs, workers, and backend infrastructure.
• Hands-on experience building AI agents, LLM-powered applications, RAG systems, workflow automation, or tool-using AI systems.
• Deep familiarity with modern agentic AI architectures, including tool/function calling, planning and execution loops, state and memory management, retrieval, structured outputs, guardrails, observability, and evaluation frameworks.
• Ability to take features from ambiguous requirements to production deployment.
• Strong software engineering fundamentals: system design, testing, debugging, performance, reliability, and maintainability.
• Experience integrating AI systems with real products, databases, APIs, and operational workflows.
• Strong product sense and ability to identify where AI agents create practical customer or business value.
• Startup mindset: high ownership, bias to shipping, comfort with ambiguity, and ability to operate with limited direction.
• Experience working in a fast-moving Seed, Series A, or similarly early-stage startup environment.

Preferred / nice-to-have

• Experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or similar.
• Experience with LLM APIs such as OpenAI, Anthropic, Google Gemini, or open-source model deployments.
• Experience with vector databases and search systems such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, Elasticsearch, or OpenSearch.
• Experience building AI evaluation pipelines, agent test harnesses, prompt regression systems, or human-in-the-loop review workflows.
• Experience with backend infrastructure such as Postgres, Redis, queues/workers, event pipelines, object storage, and cloud services.
• Experience with AWS, GCP, or Azure deployment patterns for Python services.
• Experience building AI systems for B2B SaaS, enterprise software, infrastructure, telecom, networking, or data platforms.
• Familiarity with observability tools for AI systems, including tracing, latency monitoring, cost tracking, and quality evaluation.
• Experience with secure AI system design, including permissions, data access boundaries, audit logs, and safe tool execution.

Culture & operating principles

• Ownership is real: if an agent fails, behaves unpredictably, or creates user confusion, we debug it, fix it, and harden the system.
• Bias to shipping: meaningful progress weekly; fast prototypes; production mindset; tight feedback loops.
• Practical AI over hype: we build systems that work reliably, not demos that only look good once.
• High standards on reliability: correctness, grounding, evals, and observability matter.
• End-to-end thinking: agents, backend systems, product workflows, and user experience are one system.
• Low ego, high velocity: debate the idea, be direct and respectful, and keep moving.

What success looks like

• Multiple production AI agents are shipped and actively used across product and internal workflows.
• Agent workflows are reliable, observable, and measurable through evals and execution traces.
• AI features move from prototype to production quickly without becoming fragile one-off demos.
• Agents can safely use tools, retrieve context, produce structured outputs, and integrate with backend services.
• Evals and regression testing become part of the AI development lifecycle.
• Product and engineering teams can confidently identify, build, and expand agentic AI use cases.

About Eino

Eino is building the world’s first Connectivity Digital Twin — a deeply technical, AI-driven simulation platform that models real-world environments and their connectivity layers. We solve hard, physical-world problems using advanced AI, high-performance backend systems, geometric computation, and rich large-scale data.

We are a real AI company: not LLM-wrapper tooling, but deep tech. Our work spans geometry processing, spatial reasoning, GPU-accelerated simulation, data modeling, and agentic AI systems. Every engineer at Eino works on highly challenging problems and collaborates with a team of exceptional, experienced builders.

We are well-funded by strong investors, have real customers, and a clear roadmap to reshape how the world understands connectivity.

This is a rare opportunity for a hungry, entrepreneurial AI engineer to join a rocket-ship Seed/Series A startup at the ground level, help architect our agentic AI systems, and grow into the owner of critical AI product infrastructure.