AI Engineer

Descrição do trabalho

Position Summary

As an AI Engineer, your mission will be to design, build, and deploy intelligent agents that use state-of-the-art LLMs and frameworks. You’ll own full agent lifecycles: from scoping and architecture to deployment and monitoring.

Key Responsibilities

Agent Design & Architecture

o Define agent types (reactive/proactive/tool-augmented/RAG) based on business needs, leveraging Azure’s event-driven and microservices architecture

o Select appropriate LLMs (GPT-4o, Gemini, Claude, LLaMA, etc.)

o Design orchestrators using LangChain, Semantic Kernel, or custom logic, deployed on Azure Functions or Azure Container Apps

o Architect memory systems (buffer, Redis, MongoDB, summary memory)

Data & Embedding Pipelines

o Prepare structured (CSVs, databases) and unstructured knowledge (PDFs, FAQs)

o Apply chunking strategies (paragraphs, sliding windows, recursive splitters, etc.)

o Generate embeddings (e.g., text-embedding-3-small, all-MiniLM-L6-v2, etc.)

o Index content into vector stores like Azure Search AI, Pinecone, MongoDB, or FAISS, etc.

Agent Implementation

o Use tools like LangGraph, Flowise, AgentExecutor, Haystack, etc.

o Integrate APIs, databases, web scraping, and OCR tools.

o Implement planning and tool use strategies (e.g., ReAct, Pydantic planners, etc.)

Interfaces & Deployment

o Build REST APIs or chat interfaces (Twilio, Slack, etc.) hosted on Azure App Service or Azure Container Apps

o Host on GCP, Azure, or via Docker

o Connect user sessions to memory and file uploads, etc.

Testing, Monitoring, and Feedback Loops

o Evaluate: hallucinations, relevance, F1-score, etc, using Azure Monitor and Application Insights

o Track user sessions, fallback rates, and latency

o Apply continuous learning from real user logs and feedback

Qualifications and Competencies

A Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related technical field.

3 to 5 years of hands-on experience in AI Engineering, with a proven track record of developing and deploying LLM-powered agents.

Proficiency in Python, particularly with frameworks such as LangChain, LangGraph, and Semantic Kernel.

Proficiency in Node.js and TypeScript for backend and API development, and React for building modern, responsive user interfaces.

Experience with Git for version control and implementing CI/CD workflows using GitHub Actions or similar tools to automate build, test, and deployment processes to Azure services.

Hands-on experience deploying full-stack applications (React frontend, Node.js/TypeScript backend) to Azure, leveraging Azure’s cloud-native services for secure, scalable, and automated deployments.

Solid experience working with Large Language Models (LLMs), embeddings, vector databases, and inference pipelines.

A deep understanding of AI agent architectures, including decision-making flows, tool integration, and memory management.

Demonstrated ability to design, deploy, and maintain production-grade ML/AI systems that are scalable and reliable.

Nice to Have:

o Experience with Flowise AI, AutoGen Studio, or similar agent orchestration frameworks.

o Background in building tools or integrations with platforms like Shopify, Zapier, WhatsApp Business, or Make.com.

o Familiarity with containerized environments and edge hosting solutions such as Docker, Railway, or Hugging Face Spaces.

Why Join Us?

• At Inovretail, we’re passionate about building real AI solutions that drive impact. As an AI Engineer, you’ll:
• Work on meaningful projects with real-world impact
• Tackle complex challenges across industries
• Grow with a supportive, collaborative team
• Help guide clients through their AI journey
• We value creativity, learning, and a fun, dynamic environment. If you're looking for purpose and great people, we’d love to meet you!