Algoleap Technologies is seeking a hands-on AI Engineer to design, build, and deploy production-grade agentic workflows and intelligent AI solutions. The role involves bridging modern open-source agent frameworks with enterprise AI ecosystems to create scalable, stateful AI systems. Key Responsibilities: Design and implement stateful, multi-agent orchestrations using LangGraph for complex reasoning and task decomposition. Build and maintain low-code/pro-code AI assistants using Microsoft Copilot Studio, integrating them with business systems and APIs. Develop production-ready Python backend pipelines and custom tool extensions for LLMs. Optimize system prompts, context windows, and memory management for reliability and low latency. Required Qualifications: 8-12 years of overall experience. Advanced Python proficiency (3+ years) with AsyncIO, FastAPI/Flask, and Pydantic. Hands-on expertise in LangGraph for cyclic agentic graphs and multi-agent coordination. Demonstrated experience configuring and deploying agents in Microsoft Copilot Studio, including Power Platform connectors and Azure OpenAI integrations. Experience integrating REST APIs and vector databases into LLM workflows. Preferred Qualifications: Experience with Azure AI Services or Microsoft 365 ecosystem integrations. Familiarity with evaluation frameworks like LangSmith and DeepEval, and CI/CD pipelines for AI applications. The company offers a collaborative, engineering-first culture and competitive compensation.
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Skills for this role
PythonLangGraphMicrosoft Copilot StudioAsyncIOFastAPIFlaskPydanticAzure OpenAIREST APIsVector DatabasesAzure AI ServicesLangSmithDeepEvalCI/CD
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A focused preparation guide, built around this job’s responsibilities and requirements.
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01
Understand the role
What matters most
Architecting Stateful Multi-Agent Systems
The role requires designing complex, cyclic agentic workflows using LangGraph. You must demonstrate the ability to manage state, handle task decomposition, and implement human-in-the-loop controls to ensure reliable reasoning in production environments.
Bridging Enterprise Ecosystems with AI
You must integrate Microsoft Copilot Studio with existing business systems and APIs. This involves balancing low-code/pro-code configurations with custom Python backend pipelines to ensure seamless data flow and enterprise-grade functionality.
Optimizing LLM Performance and Reliability
A core priority is the technical optimization of AI systems, specifically focusing on prompt engineering, context window management, and memory efficiency to achieve low-latency, production-ready performance.
Likely challenges to think through
Managing Complexity in Cyclic Agentic Graphs
Designing cyclic graphs in LangGraph can lead to unpredictable agent behavior or infinite loops. Prepare by reviewing strategies for state persistence, error handling, and implementing guardrails to maintain system stability.
Balancing Low-Code Constraints with Custom Logic
Integrating Microsoft Copilot Studio with custom Python-based tool extensions may create friction between platform limitations and bespoke requirements. Be prepared to discuss how you bridge these environments while maintaining clean, maintainable code.
Ensuring Deterministic Output in Non-Deterministic Systems
Production-grade AI requires high reliability, which is difficult with LLMs. Prepare to discuss your experience with evaluation frameworks like LangSmith or DeepEval to measure performance and validate agent outputs before deployment.
03
Practice your answers
Technical practice
Designing Stateful Multi-Agent Orchestrations with LangGraph
What to coverExplain how you manage state persistence in cyclic agentic graphs. Discuss the trade-offs between using in-memory state versus external databases for long-running agent workflows and how you handle human-in-the-loop interruptions.
Optimizing LLM Performance and Latency
What to coverDescribe your approach to optimizing system prompts and context window management for production-grade applications. Focus on techniques for reducing latency while maintaining high reasoning reliability in multi-agent systems.
Integrating Microsoft Copilot Studio with Enterprise Systems
What to coverDetail your experience extending Copilot Studio using custom tool extensions and Power Platform connectors. Explain how you ensure secure authentication and data flow when connecting these assistants to internal business APIs.
Python Backend Engineering for AI Pipelines
What to coverDiscuss how you leverage AsyncIO and FastAPI to build scalable, production-ready backend services for AI applications. Provide an example of how you use Pydantic for robust data validation in complex LLM request/response cycles.
Implementing RAG and Vector Database Workflows
What to coverExplain your strategy for integrating vector databases into LLM workflows to improve retrieval accuracy. Address how you handle data indexing, chunking strategies, and retrieval evaluation to minimize hallucinations.
Evaluation Frameworks for AI Applications
What to coverHow do you utilize frameworks like LangSmith or DeepEval to monitor and evaluate agent performance? Describe a scenario where you used these tools to identify and fix a regression in an agent's reasoning capability.
CI/CD for Agentic Systems
What to coverWhat are the unique challenges of implementing CI/CD pipelines for non-deterministic AI applications? Suggest how you would automate testing for agentic workflows to ensure reliability before deployment.
Handling Complex Task Decomposition
What to coverDescribe your methodology for breaking down complex user requests into manageable sub-tasks within a multi-agent architecture. Discuss how you manage dependencies between agents and ensure consistent state across the execution flow.
Behavioural practice
Navigating Technical Ambiguity in AI Projects
What to coverDescribe a situation where you had to design an AI solution with unclear or evolving requirements. Explain how you defined the scope, managed stakeholder expectations, and ensured the final architecture remained scalable.
Collaborating in an Engineering-First Culture
What to coverProvide an example of how you have contributed to a collaborative, high-impact engineering team. Focus on how you shared knowledge, performed code reviews, or mentored others to improve overall team output.
Managing Production Incidents in AI Systems
What to coverTell us about a time an AI system you deployed failed or behaved unexpectedly in production. Detail the steps you took to diagnose the issue, the immediate fix, and the long-term changes you implemented to prevent recurrence.
Balancing Speed and Quality in Development
What to coverDescribe a project where you had to deliver a production-grade AI solution under tight deadlines. How did you decide which features to prioritize and what technical debt, if any, you were willing to accept?
02
Sharpen your knowledge
Topics to prepare
Stateful Multi-Agent Orchestration
Focus on the conceptual design of cyclic agentic graphs and state management. Exercise: Draft a high-level architecture for a multi-agent system where one agent handles task decomposition and another manages tool execution, ensuring state persistence between steps. This is critical for the role's requirement to build complex, stateful AI systems using LangGraph.
Enterprise AI Integration Patterns
Study the integration of LLMs with business systems, specifically focusing on RAG and API-driven tool extensions. Exercise: Map out how a custom Python backend would securely interface with a vector database and an external REST API to provide context-aware responses. This aligns with the need to bridge open-source frameworks with enterprise ecosystems.
Skills to revise
LangGraph Implementation
Practice building cyclic graphs that include human-in-the-loop checkpoints. Exercise: Create a small LangGraph workflow that requires a human to approve an action before the agent proceeds to the next node. This directly addresses the requirement for hands-on expertise in multi-agent coordination.
Microsoft Copilot Studio Configuration
Gain proficiency in extending Copilot agents with custom logic and Azure OpenAI integrations. Exercise: Configure a custom topic in Copilot Studio that triggers a Power Platform connector to fetch data from a business system. This is essential for the role's focus on low-code/pro-code AI assistant development.
Advanced Python Backend Development
Focus on building asynchronous, production-ready services. Exercise: Develop a FastAPI endpoint that uses Pydantic for request validation and AsyncIO to handle concurrent LLM tool calls. This is a core requirement for the backend engineering component of the role.
04
Your preparation checklist
Demonstrate LangGraph proficiency
Prepare a technical walkthrough of a project where you implemented a cyclic agentic graph. Focus on how you managed state, implemented human-in-the-loop checkpoints, and coordinated multiple agents to solve a complex task.
Showcase Microsoft Copilot Studio integration
Prepare a summary of your experience extending Copilot Studio. Highlight specific examples of using Power Platform connectors, custom API integrations, and Azure OpenAI configurations to solve enterprise business problems.
Review Python backend engineering standards
Be ready to discuss your experience building production-grade services using FastAPI or Flask. Prepare to explain how you utilize AsyncIO for concurrency and Pydantic for robust data validation in high-throughput AI pipelines.
Prepare for system optimization discussions
Develop a strategy for explaining your approach to LLM reliability. Be prepared to discuss techniques for optimizing system prompts, managing context window constraints, and reducing latency in RAG or agentic workflows.
Formulate targeted questions for the team
Prepare questions regarding their current agentic architecture, such as how they handle evaluation at scale using frameworks like LangSmith or DeepEval, and how they balance low-code Copilot solutions with custom Python-based agentic logic.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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