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 Processing

- 3.5 to 4 yrs proficiency with PyTorch or TensorFlow/Keras

- Candidates from engineering product companies have higher chances of getting shortlisted (current company or past experience)