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.
