Lead Data Science Algorithm Image Processing

Must Have:

• Education/qualification: Preferably from an Institute like IIT, IISC, IIIT, NIT and BITS.
  Doctorate (Academic) Degree and 2 years of 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
• 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

Job Description/Preferred Qualifications

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.