Must have Skills :

- Mastery of Python and strong familiarity with libraries such as NumPy, Pandas, and Scikit-learn.

- Extensive hands-on experience with TensorFlow (preferred) or PyTorch (Experience with both is a strong plus).

- Strong knowledge of Pattern Recognition and Neural Networks.

- Solid foundation in Computer Science and Algorithms.

- Proficiency in Statistics and machine learning concepts.

- Experience in deploying machine learning models in production environments.

- Strong understanding of NLP techniques (Tokenization, Embeddings, Transformers, Attention Mechanisms).

- Proficiency in SQL and experience handling large datasets.

Good to have :

- GenAI Stack: Experience with frameworks like LangChain, LlamaIndex, or Haystack.

- Vector Databases: Hands-on experience with vector stores such as Pinecone, Milvus, Weaviate, ChromaDB or FAISS.

- Model Tuning: Proven track record of fine-tuning open-source models (e.g., Hugging Face transformers) on custom datasets.

- Cloud AI: Experience with AWS SageMaker, Azure AI Studio, or Google Vertex AI.

- Big Data: Experience handling large-scale datasets using Apache Spark or Databricks.

Soft Skills & Competencies :

- Problem Solver: Ability to break down ambiguous problems into solvable algorithmic components.

- Continuous Learner: The AI landscape changes weekly; you must demonstrate a hunger to keep up with the latest papers and techniques.

- Communication: Ability to explain complex model behaviors to non-technical stakeholders.