As an AI Native Software Engineer, you will design, build, and deploy applications where artificial intelligence is deeply integrated into every stage of development. You will collaborate with cross-functional teams to innovate and improve the software lifecycle, ensuring solutions meet high standards of quality and compliance.
Responsibilities
Design and implement multi-agent orchestration pipelines using LangGraph state machines with conditional routing and self-healing loops.
Build and maintain hybrid retrieval systems combining Neo4j knowledge graphs with Qdrant vector search and sentence-transformer embeddings.
Develop FastAPI backend services with async I/O, Pydantic v2 models, and JWT/RBAC security.
Build React 18 and TypeScript frontend features including visual graph editors, real-time SSE streaming, and observability dashboards.
Integrate enterprise protocols such as A2A, MCP, and AG-UI, and connect to ALM/PLM tools via REST and GraphQL APIs.
Implement dual-tier agent memory with semantic recall and context consolidation.
Build DAG-based workflow compilation with topological sorting and Ray-based parallel execution.
Deploy and operate containerized services using Docker, Azure Bicep, and CI/CD pipelines.
Serve and manage on-prem LLM models via vLLM alongside cloud-hosted models.
Required qualifications
4 years of professional software engineering experience.
2 years of experience working with LLMs, agent frameworks, or ML systems in production.
Strong proficiency in async Python and modern React/TypeScript.
Experience with graph databases or knowledge graph systems.
Ability to own features across the full stack from API to UI to deployment.
15 years of full-time education.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Multi-Agent Orchestration and State Management
The role requires designing complex pipelines using LangGraph state machines. You must prioritize building self-healing loops and conditional routing to ensure agent reliability. Focus your preparation on demonstrating how to manage state transitions and handle failures in autonomous agent workflows.
Hybrid Retrieval and Knowledge Graph Integration
You are expected to bridge unstructured data via Qdrant vector search with structured knowledge via Neo4j. Success depends on your ability to implement effective RAG architectures, including chunking strategies and semantic recall, to provide agents with accurate, context-aware information.
Full-Stack AI-Native Development
You must own the entire lifecycle, from FastAPI backend services with async I/O to React 18/TypeScript frontends featuring real-time SSE streaming. Prioritize demonstrating your ability to connect these components securely using OAuth2/SSO and enterprise protocols like MCP.
Production-Grade LLM Operations
Beyond development, you must manage on-prem LLM serving via vLLM and optimize token budgeting. Preparation should focus on the trade-offs between cloud-hosted and on-prem models, including latency, cost, and compliance requirements in an enterprise environment.
Likely challenges to think through
Balancing Agent Autonomy with System Stability
Implementing self-healing loops and multi-agent hierarchies introduces non-deterministic behavior. The challenge is ensuring these systems remain observable and compliant. Prepare by discussing how you implement logging, tracing, and human-in-the-loop checkpoints to maintain control over autonomous processes.
Complexity of Hybrid Data Architectures
Integrating Neo4j graph traversals with vector-based semantic search requires sophisticated query optimization. You may face performance bottlenecks when scaling these systems. Prepare by explaining how you handle data synchronization and latency issues between graph and vector stores.
Managing Real-Time UI/UX for AI Agents
Building visual graph editors and real-time observability dashboards requires high-performance frontend state management. The challenge is keeping the UI synchronized with asynchronous backend agent streams. Prepare by discussing your approach to managing server-state and real-time data flow using tools like TanStack Query or Zustand.
03
Practice your answers
Technical practice
Designing Multi-Agent Orchestration with LangGraph
What to coverExplain how you would implement a state machine in LangGraph to handle conditional routing and self-healing loops. Discuss the trade-offs between using a centralized orchestrator versus a decentralized sub-agent hierarchy for complex tasks.
Hybrid Retrieval Systems: Neo4j and Qdrant
What to coverDescribe your approach to building a hybrid retrieval system that combines structured knowledge graphs with unstructured vector search. How do you manage the integration of Cypher queries with semantic similarity search to improve RAG accuracy?
Async FastAPI and Pydantic v2 Performance
What to coverDiscuss the benefits of using async I/O patterns in FastAPI for high-concurrency applications. Explain how you leverage Pydantic v2 models to ensure data validation and performance in production-grade backend services.
Real-time Frontend Streaming with React and SSE
What to coverHow do you implement real-time Server-Sent Events (SSE) in a React 18/TypeScript application to display streaming LLM responses? Address how you manage state updates and UI responsiveness during long-running agentic processes.
DAG-based Workflow Compilation and Parallel Execution
What to coverExplain how you would design a DAG-based workflow compiler that supports topological sorting and cycle detection. How does Ray facilitate parallel execution in this context, and what are the challenges in managing state across distributed tasks?
Dual-Tier Agent Memory Management
What to coverDescribe the architecture of a dual-tier memory system using Redis for short-term recall and a persistent layer like mem0 for long-term context. How do you handle context consolidation to prevent token budget overflow?
On-prem LLM Serving with vLLM
What to coverWhat are the primary considerations when serving LLMs on-premise using vLLM compared to cloud-hosted APIs? Discuss strategies for model optimization, throughput management, and handling multi-model routing.
Enterprise Security and Protocol Integration
What to coverHow do you secure FastAPI services using JWT and RBAC while integrating with enterprise SSO providers like OIDC or SAML? Explain your experience with agentic protocols like MCP or A2A for interoperability.
Infrastructure as Code and CI/CD for AI Systems
What to coverDescribe your experience using Azure Bicep for IaC and how you structure CI/CD pipelines to handle the deployment of containerized AI services. How do you ensure reproducibility and compliance in your deployment lifecycle?
Behavioural practice
Collaborating in Cross-Functional AI Projects
What to coverDescribe a situation where you had to collaborate with non-technical stakeholders to define the requirements for an AI-integrated feature. How did you balance technical feasibility with business needs?
Handling Technical Debt in Rapidly Evolving AI Systems
What to coverTell me about a time you identified a bottleneck or technical debt in an existing ML or agentic pipeline. What steps did you take to refactor the system while maintaining service availability?
Owning End-to-End Feature Delivery
What to coverProvide an example of a project where you owned the full stack from API design to UI implementation and deployment. What were the biggest challenges you faced in maintaining consistency across these layers?
Adapting to Emerging AI Technologies
What to coverDescribe a time you had to quickly learn and integrate a new framework or protocol (such as a new agentic protocol or graph database) into a production environment. How did you validate its reliability before full-scale adoption?
02
Sharpen your knowledge
Topics to prepare
Multi-Agent Orchestration and State Machines
The role requires designing pipelines using LangGraph. Review the conceptual framework of state machines, conditional routing, and self-healing loops. Practice by mapping out a workflow that handles agent failures and retries using state-based logic.
Hybrid Retrieval Systems (RAG)
Understand the integration of Neo4j knowledge graphs with Qdrant vector search. Focus on how semantic recall and context consolidation work together. Review how to combine structured Cypher queries with unstructured vector similarity search to improve LLM response accuracy.
Agentic Interoperability Protocols
Familiarize yourself with emerging standards like MCP (Model Context Protocol) and A2A. Research how these protocols facilitate communication between different AI agents and external tools, which is a specific requirement for this role.
Skills to revise
Async Python and FastAPI
Develop a small service using FastAPI that utilizes async I/O and Pydantic v2 models. Ensure you can implement JWT-based authentication and handle asynchronous database operations, as these are core to the backend requirements.
React 18 and Real-time UI
Build a prototype using React 18 and TypeScript that consumes a stream of data via Server-Sent Events (SSE). Practice integrating a visual graph library like ReactFlow to demonstrate your ability to create interactive observability dashboards.
Containerization and IaC
Practice deploying a containerized application using Docker. Gain familiarity with Azure Bicep for infrastructure as code to ensure you can manage the deployment lifecycle within a CI/CD pipeline environment.
04
Your preparation checklist
Architect a Multi-Agent System Walkthrough
Prepare a technical overview of a project where you implemented a LangGraph state machine. Focus on how you handled conditional routing, self-healing loops, and state persistence to ensure reliable agentic workflows.
Demonstrate Hybrid Retrieval Expertise
Create a brief explanation of a RAG system you built that integrates a graph database (like Neo4j) with a vector database (like Qdrant). Be prepared to discuss your approach to entity-relationship modeling and how you combined semantic search with graph traversal.
Showcase Full-Stack Async Proficiency
Prepare code snippets or a high-level design document showing a FastAPI backend using async I/O and Pydantic v2, connected to a React 18/TypeScript frontend that utilizes real-time streaming (SSE) or interactive graph visualizations.
Prepare for Infrastructure and Deployment Questions
Review your experience with containerization and IaC. Be ready to explain how you have managed CI/CD pipelines (e.g., Azure Pipelines or Jenkins) and deployed services using tools like Docker and Azure Bicep.
Formulate Strategic Questions for the Interviewer
Prepare questions regarding the team's current approach to LLM model serving (e.g., vLLM usage), how they manage enterprise security protocols like OAuth2/OIDC in agentic systems, and their strategy for long-term agent memory consolidation.
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