Lead Analyst, AI Engineer (Data Science)

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; Masters Level Degree and related work experience of 5 years; Bachelors Level Degree and related work experience of 7 years in building AI systems/solutions with Machine Learning, Deep Learning, and LLMs.