Kadel Labs
Website:
kadellabs.com
Job details:
Kadel Labs is a deep-tech IT services company dedicated to transforming businesses with cutting-edge technology solutions. Our expertise spans Data Analytics, Engineering, and Visualization; Cloud services including DevOps, InfoSecOps, and InfraOps; Software development and modernization; and Generative AI-driven business applications.
As a trusted partner of Databricks, Microsoft Azure, Amazon Web Services, Software AG webMethods, and Bitrix24, we empower businesses with innovative, reliable, and scalable technology solutions that drive digital transformation and long-term growth.
Job Overview
We are looking for a skilled Generative AI Engineer to design, develop, and deploy AI-driven solutions using Large Language Models (LLMs) and modern AI frameworks. The ideal candidate will have hands-on experience building AI applications, chatbots, and automation solutions using Generative AI technologies.
Key Responsibilities
- Develop and implement Generative AI solutions using Large Language Models (LLMs).
- Build and optimize AI-powered applications, chatbots, and automation tools.
- Work with frameworks such as LangChain, LlamaIndex, or similar AI orchestration tools.
- Fine-tune and evaluate machine learning and NLP models.
- Integrate AI solutions with existing platforms through APIs and microservices.
- Collaborate with data engineers, developers, and product teams to deliver scalable solutions.
- Ensure performance, security, and scalability of AI applications.
- Stay updated with the latest trends and innovations in AI, ML, and Generative AI technologies.
Required Skills set
- Strong proficiency in Python programming.
- Hands-on experience with LLMs and Prompt Engineering.
- Experience with LangChain, Hugging Face, or LlamaIndex frameworks.
- Knowledge of Machine Learning, NLP, and Deep Learning concepts.
- Experience with vector databases such as Pinecone, Weaviate, or FAISS.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Understanding of REST APIs, Docker, and scalable architectures.
- Experience with RAG (Retrieval-Augmented Generation) architectures.
- Exposure to AI model deployment and MLOps tools.
- Knowledge of data engineering pipelines and big data ecosystems.
Click on Apply to know more.