Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q5jgu

Full-stack & AI Integration Engineer

Role: Full-stack & AI Integration Engineer

Location: Remote--will work for a US client with teams based in Los Angeles, California

Level: Senior

Department: Engineering

Summary

Our client is revolutionizing marketing with the first AI marketing agent that merges human insights with advanced AI to address organizational complexity. The technology streamlines processes and accelerates audience targeting from months to weeks. Serving marketers in sports, entertainment, fashion, and travel, the platform deciphers complex business objectives to deliver actionable insights for hyper-personalized strategies. Endorsed by the New York Mets and powered by cutting-edge ML architecture, the platform is set to transform the marketing landscape.

About the Project

They are scaling their machine learning models and enhancing their platform’s capabilities. Built using a sophisticated architecture, the ML models optimize and accelerate audience targeting through actionable insights. Their goal is to develop a robust, scalable platform that integrates these models seamlessly and enhances their accessibility across diverse industries. This platform will be the cornerstone of their AI-driven services, facilitating real-time data processing, complex algorithmic computations, and a user-friendly interface that empowers marketers to execute hyper-personalized strategies effectively.

What you’ll do: 1. Set Up an API for Customized AI Algorithms

- Conversation Analysis: Use NLP techniques to analyze user inputs and extract key information.
- Entity Extraction and Intent Recognition: Implement entity extraction and intent recognition to understand user needs (skills: spa Cy, NLTK, Hugging Face transformers).
- API Development: Develop a secure and well-documented API for the ML models using frameworks like Flask, FastAPI,



or Django.
- Dynamic Algorithm Selection: Implement a system for selecting appropriate algorithms based on user needs.
- Customized Response Generation: Generate user-friendly responses based on algorithm outputs.
- Input/Output Handling: Define and handle input and output formats for each algorithm.

2. Integrate Gen AI Agent with Insight Box API

- Custom Application: Create a custom application that interfaces between the Gen AI agent and the API.

3. Deploy and Test

- Deployment: Deploy the complete API and custom Gen AI application on cloud platforms like AWS, GCP, or Azure.
- Ensure Scalability and Reliability: Set up monitoring and logging.
- Testing: Conduct extensive testing to ensure correct interpretation of user inputs, appropriate algorithm selection, and accurate responses.

4. Iterate and Improve

- User Feedback: Collect and incorporate user feedback to improve interactions and algorithm selection.
- Continuous Integration/Continuous Deployment (CI/CD): Implement a CI/CD pipeline to continuously update and improve the system.

5. Design, Develop, and Maintain the Platform

- Front-End and Back-End Development: Ensure the integration of the ML models with backend services for smooth data flow and operation.
- Collaborate with UX/UI Designers: Implement a seamless and dynamic user experience.

Your background and skills will include:

- Proficiency in multiple programming languages (Python, Java Script/Type Script).




- Expertise in both front-end and back-end development.
- Some knowledge of machine learning frameworks and natural language processing (NLP) tools.
- Experience with cloud platforms (AWS, GCP, Azure) and Dev Ops practices.
- The engineer needs to have practical experience with building and deploying machine learning models.
- Some understanding of AI algorithms and ability to integrate them into applications.
- Experience with developing and managing RESTful APIs and possibly GraphQL.
- Proficiency in containerization (Docker, Kubernetes) and setting up CI/CD pipelines.
- Ability to collaborate with UX/UI designers.
- Strong problem-solving skills and adaptability to new technologies and tools.
- Tools and frameworks:
- Python: Tensor Flow, PyTorch, scikit-learn, spa Cy, NLTK, Hugging Face transformers, Flask, FastAPI, for building and deploying models.
- Java Script/Type Script: For front-end interfaces and API integration. Frameworks: React, Node.js
- API Development and Integration: RESTful APIs (Flask, FastAPI), GraphQL
- Data Modelling and Analytics: Designing and manipulating databases.
- SQL/NoSQL: Experienced in both structured (PostgreSQL, MySQL) and unstructured (MongoDB, Elasticsearch) database systems.
- AI and Digital Technology: Familiarity with machine learning frameworks and integration into user-facing applications.
- Cloud Services: AWS, GCP for deploying APIs, databases, and machine learning models. Services: Lambda, EC2, S3, RDS
- Dev Ops and CI/CD: Docker, Kubernetes for containerization and orchestration; Jenkins, Git Hub Actions, or Git Lab CI for continuous integration and deployment.
- Intelligent User Interfaces (IUI): Skilled in creating adaptive and intuitive user interfaces.

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Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q5jgu

📌 Full-stack & AI Integration Engineer (Argentina)
🏢 Trades Workforce Solutions
📍 Argentina