Design and implement RAG-based solutions to enhance LLM capabilities with external knowledge sourcesDevelop and optimize LLM fine-tuning strategies for specific use cases and domain adaptationCreate robust evaluation frameworks for measuring and improving model performanceBuild and maintain agentic workflows for autonomous AI systemsCollaborate with cross-functional teams to identify opportunities and implement AI solutionsRequired Qualifications :
- Bachelor's or Master's degree in Computer Science, or related technical field
- 3+ years of experience in Machine Learning / AI engineering
- Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow)
- Practical experience with LLM deployments and fine-tuning
- Experience with vector databases and embedding models
- Familiarity with modern AI / ML infrastructure and cloud platforms (AWS, GCP, Azure)
- Strong understanding of RAG architectures and implementation
Preferred Qualifications :
- Experience with popular LLM frameworks (Langchain, LlamaIndex, Transformers)
- Knowledge of prompt engineering and chain-of-thought techniques
- Experience with containerization and microservices architecture
- Background in NLP and deep learning
- Background in Reinforcement Learning
- Contributions to open-source AI projects
- Experience with ML ops and model deployment pipelines
Skills and Competencies :
- Strong problem-solving and analytical skills
- Excellent communication and collaboration abilities
- Experience with agile development methodologies
- Ability to balance multiple projects and priorities
- Strong focus on code quality and best practices
- Understanding of AI ethics and responsible AI development
Skills Required
Machine Learning, Python