We are seeking an innovative Generative AI Engineer with strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Python, and Machine Learning . The ideal candidate will be responsible for designing, developing, and deploying AI-powered applications that leverage state-of-the-art language models to solve complex business problems.
The candidate should have hands-on experience with LLM integration, prompt engineering, vector databases, RAG pipelines, machine learning model development, and cloud-based AI services. This role requires close collaboration with data scientists, software engineers, product teams, and business stakeholders to build scalable AI solutions.
Key ResponsibilitiesGenerative AI Development
- Design, develop, and deploy Generative AI solutions using modern LLM frameworks.
- Build AI-powered applications such as intelligent chatbots, virtual assistants, document processing systems, and content generation platforms.
- Integrate foundation models through APIs and open-source frameworks.
- Optimize AI applications for scalability, performance, and cost efficiency.
- Stay updated with the latest advancements in Generative AI technologies.
Large Language Models (LLMs)
- Develop applications utilizing commercial and open-source LLMs.
- Fine-tune, evaluate, and optimize language models where applicable.
- Design effective prompt engineering strategies for improved model responses.
- Implement model monitoring, evaluation, and response quality metrics.
- Address AI safety, hallucination reduction, and responsible AI practices.
Retrieval-Augmented Generation (RAG)
- Design and implement Retrieval-Augmented Generation (RAG) pipelines.
- Build document ingestion, embedding, indexing, and retrieval workflows.
- Integrate vector databases for semantic search and knowledge retrieval.
- Optimize retrieval accuracy, context management, and response relevance.
- Work with structured and unstructured enterprise data sources.
Machine Learning & Data Science
- Develop, train, evaluate, and deploy machine learning models.
- Perform data preprocessing, feature engineering, and model validation.
- Implement NLP and text analytics solutions.
- Analyze model performance and improve prediction accuracy.
- Collaborate with data engineering teams to build scalable ML pipelines.
Python Development
- Develop scalable backend services and AI applications using Python.
- Build REST APIs for AI model integration.
- Write clean, modular, and maintainable production-grade code.
- Develop reusable libraries and automation scripts.
- Integrate AI solutions with enterprise applications.
Deployment & MLOps