Artificial Intelligence Engineer - RAG/LLM Models

Role Summary :

MOURITech is seeking an innovative and highly skilled AI Engineer to design, develop, and deploy autonomous AI agents and production-ready AI applications that elevate our Data Science and Business Intelligence capabilities. This role will partner with Data Science, Product, Engineering, and the Information Management & Analytics team to deliver scalable agent-based solutions across marketing, sales, operations, and member experience.

The ideal candidate combines deep expertise in large language models (LLMs), multi-agent orchestration, and retrieval-augmented generation (RAG) with hands-on proficiency in AI-assisted development tools such as Cursor and GitHub Copilot. They will build, test, and maintain agent workflows that automate complex business processes while ensuring security, reliability, responsible AI practices, and enterprise-grade quality.

Key Responsibilities :

AI Agent Engineering :

- Design, build, and deploy autonomous AI agents using LLMs, tool-calling, RAG, and multi-agent orchestration patterns (e.g., planning agents, execution agents, validation agents).

- Build production-ready agent applications with proper error handling, guardrails, retry logic, and observability (logging, tracing, metrics).

- Leverage AI-assisted development tools (Cursor Agent Mode, GitHub Copilot) to accelerate delivery and maintain high code quality through rules, skills, and context management.

- Translate business problems into technical agent designs, defining agent roles, tool inventories, memory strategies, and escalation paths.

Agent Development & Optimization :

- Architect multi-step agent workflows with planning, tool use, memory management, and human-in-the-loop checkpoints.

- Implement prompt engineering best practices, evaluation frameworks, and systematic agent testing strategies (unit, integration, end-to-end).

- Build and maintain reusable agent templates, Cursor skills and rules libraries, and shared prompt catalogs for team-wide productivity.

- Run experiments, benchmark agent performance, and document results and recommendations for continuous improvement.

Production Integration :

- Work with ML Ops and DevOps to operationalize agent applications through CI/CD pipelines, model registries, and repeatable deployment processes.

- Develop APIs, conversational interfaces, and batch automation pipelines that expose agent capabilities to business users and downstream systems.

- Ensure agent outputs are reliable, auditable, and meet performance, latency, and cost targets in production environments.

- Implement monitoring and alerting for agent health, token usage, error rates, and output quality in real-time.

Governance & Responsible AI :

- Apply responsible AI practices: bias assessment, explainability, output validation, and model risk documentation.

- Implement agent-specific governance controls including hallucination prevention, content filtering, human-in-the-loop approval gates, and audit trails for all agent actions.

- Ensure compliance with data privacy, security, and internal standards across all agent interactions and data flows.

Mandatory Required skills :

- 5 to 8+ years of hands-on experience in AI/ML engineering, applied machine learning, or AI application development.

- Expert Python and strong software engineering practices (testing, packaging, code review, version control).

- Proven experience building AI agents using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar.

- Proficiency with AI-assisted development tools (Cursor, GitHub Copilot) for rapid, high-quality application development including agent mode, rules configuration, and context management.

- Experience with prompt engineering, RAG architectures, vector databases, and LLM API integration (OpenAI, Azure OpenAI, Anthropic).

- Experience deploying AI applications to production (batch and/or real-time) in cloud environments (Azure preferred).

- Strong understanding of model evaluation, monitoring concepts, and data/feature quality.

Desired Skills :

- Experience building multi-agent systems with tool calling, function calling, and MCP (Model Context Protocol) integrations.

- Experience with agent evaluation and testing frameworks (e.g., LangSmith, Braintrust, custom evaluation harnesses).

- Familiarity with Cursor rules, skills, and agent mode workflows for team-scale AI-assisted development and standardized engineering practices.

- Experience creating document generation, data pipeline, or workflow automation agents for enterprise use cases.

- Experience with Azure Machine Learning, Databricks, MLflow, and vector databases (Pinecone, Weaviate, Azure AI Search).

- Experience building retrieval-augmented generation (RAG) systems and prompt evaluation pipelines.

- Strong communication skills with ability to explain technical concepts to business stakeholders.

Certifications :

- Azure AI Engineer Associate and/or relevant machine learning certifications preferred.