Description :
Required skills / Competencies :
- Programming Languages
- Strong in Python, data structures, and algorithms.
- Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.
- Machine Learning Frameworks
- Understanding of supervised / unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).
- Deep Learning Techniques
- Proficiency with PyTorch or TensorFlow / Keras
- Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.
- Familiarity with optimization (Adam, SGD), dropout, batch norm.
- LLMs & RAG
- Hugging Face Transformers (tokenizers, embeddings, model fine-tuning).
- Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).
- Prompt engineering, function / tool calling, JSON schema outputs.
- Data & Tools
- SQL fundamentals; exposure to data wrangling and pipelines.
- Git / GitHub, Jupyter, basic Docker.
What are we looking for :
Solid academic foundation with strong applied ML / DL exposure.Curiosity to learn cutting-edge AI and willingness to experiment.Clear communicator who can explain ML / LLM trade-offs simply.Strong problem-solving and ownership mindset.Minimum Qualifications :
Doctorate (Academic) Degree and 2 years related work experience; Master's Level Degree and related work experience of 5 years; Bachelor's Level Degree and related work experience of 7 years in building AI systems / solutions with Machine Learning, Deep Learning, and LLMs.
Must-Haves :
Min 5 yrs experience in the Mandatory Skills : Python, Deep Learning, Machine Learning, Algorithm Development and Image Processing3.5 to 4 yrs proficiency with PyTorch or TensorFlow / KerasCandidates from engineering product companies have higher chances of getting shortlisted (current company or past experience)(ref : hirist.tech)