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Mandatory Skills: 

1. Machine Learning & Deep Learning – Strong understanding of LLM architectures, transformers, and fine-tuning techniques.
2. MLOps & DevOps – Experience with CI/CD pipelines, model deployment, and monitoring.
3. Vector Databases – Knowledge of storing and retrieving embeddings efficiently.
4. Prompt Engineering – Ability to craft effective prompts for optimal model responses.
5. Retrieval-Augmented Generation (RAG) – Implementing techniques to enhance LLM outputs with external knowledge.
6. Cloud Platforms – Familiarity with AWS, Azure, or GCP for scalable deployments.
7. Containerization & Orchestration – Using Docker and Kubernetes for model deployment.
8. Observability & Monitoring – Tracking model performance, latency, and drift.
9. Security & Ethics – Ensuring responsible AI practices and data privacy.
10. Programming Skills – Strong proficiency in Python, SQL, and API development.

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Prefered Skills : 

1. Knowledge of Open-Source LLMs – Familiarity with models like LLaMA, Falcon, and Mistral.
2. Fine-Tuning & Optimization – Experience with LoRA, quantization, and efficient training techniques.
3. LLM Frameworks – Hands-on experience with Hugging Face, LangChain, or OpenAI APIs.
4. Data Engineering – Understanding of ETL pipelines and data preprocessing.
5. Microservices Architecture – Ability to design scalable AI-powered applications.
6. Explainability & Interpretability – Techniques for understanding and debugging LLM outputs.
7. Graph Databases – Knowledge of Neo4j or similar technologies for complex data relationships.
8. Collaboration & Communication – Ability to work with cross-functional teams and explain technical concepts clearly.

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Deliver