Senior AI Engineer

Job Description

We are seeking an experienced Senior Data Engineer to join our team and work with clients on exciting generative AI projects. Your first project will involve enhancing a GPT-3.5-based chatbot integrated into a virtual reality training platform for sales representatives. In this role, you'll focus on improving the chatbot's performance, speed, and accuracy while integrating it more effectively with Azure's cloud infrastructure.

The project is focused on developing an innovative training platform for Sales Representatives using VR and Unity technologies. It allows them to practice skills in realistic 3D environments, such as supermarkets and restaurants, by arranging products and marketing materials.

Key features include:

• Platform optimization and scalability for international use;

• Multi-user training mode for collaborative learning;

• Analytics to evaluate training effectiveness;

• Integration with AI and chatbots for real-time assistance;

• Tools for creating lessons and layouts;

• Dynamic content updates tailored by country.  

The goal is to make training more interactive and effective, preparing Sales Representatives to achieve better results in real-world settings.

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Requirements:  

• Experience: 5+ years of experience in Data engineering;

• Python Skills: Proficiency in Python for developing and optimizing data pipelines, automating workflows, and improving AI model integration;

• Education: Bachelor's degree in Computer Science, Information Technology, or a related field;

• AWS/Azure Cloud Expertise: Experience with AWS/Azure is essential (willingness to learn and work with Azure); expertise with Azure technologies such as Azure Machine Learning, Azure OpenAI Services, Azure Kubernetes Service (AKS), and Azure Blob Storage will be considered a strong plus;

• Generative AI Models: Hands-on experience working with GPT-3.5, GPT-4, or similar generative AI models (particularly through Azure OpenAI Service);

• Data management: Familiarity with vector databases, data chunking strategies, multilingual embedding models, reranking techniques, and content retrieval (RAG);

• Cloud scaling: Experience with performance management in Azure, including dynamic scaling and leveraging GPU resources for accelerated AI workloads;

• Optimization skills: In-depth knowledge of code optimization for AI, including improving speed, minimizing redundant computations, and managing memory effectively;

• Communication skills: Ability to talk with clients and teammates confidently, explain your technical decisions clearly, and create architecture diagrams to illustrate your ideas;

• Team collaboration: Ability to collaborate effectively in cross-functional teams;

• Agile experience: Skilled in working within an agile environment, adapting to evolving project needs and delivering results iteratively.

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Responsibilities:

• Optimize AI models: Work on refining generative AI solutions to reduce latency, improve content retrieval and quality of responses, and manage localized content based on different user regions;

• Improve user experience: Ensure a smooth, high-performance experience for users in various locations by using Azure cloud services to optimize the chatbot’s speed and accuracy in real-time;

• Client Interaction: You’ll have the chance to work directly with clients, explaining technical concepts in an easy-to-understand way and keeping them updated on project progress.

What we offer:  

• Opportunities for professional growth and advancement;  

• A collaborative and innovative work environment;

• 20 days of paid vacation, 15 paid days of sick leave with a doctor's note, and 5 days of paid sick leave without a doctor's note;  

• Medical insurance coverage for employees, plus an option for family insurance coverage at a corporate rate (depending on the location);  

• Support for participation in professional development opportunities (webinars, conferences, trainings, etc.);  

• Regular team-building opportunities as well as bi-annual company-wide events;  

• Flexible work environments, including in-office, remote, or hybrid, based on employee preference and manager approval.

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