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).
Robust 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, orHaystack. 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 GoogleVertex AI.
Big Data: Experience handling large-scale datasets using Apache Spark orDatabricks.
Soft Skills & Competencies:
Problem Solver: Ability to break down ambiguous problems into solvablealgorithmic components.
Continuous Learner: The AI landscape changes weekly; you must demonstratea hunger to keep up with the latest papers and techniques.
Communication: Ability to explain complex model behaviors to non-technicalstakeholders.