Senior Data Scientist - Python/LLM

Requirements :

• 7 years of experience building production ML / AI systems, with recent experience in LLMs or AI agents in data science.
• Strong Python engineer with a bias toward clean, scalable, and maintainable code.
• Proven experience working with unstructured data and images at scale.
• Hands-on experience with agent patterns (tool use, function calling, planning, memory).
• Good proficiency and hands-on experience with LLM's and advanced AI models.
• Experience deploying AI systems on AWS, GCP, or Azure in microservices architectures.
• Thrives in fast-moving startup environments with high ownership and ambiguity.
• Strong Python skills with focus in data engineering and data analysis, including proficiency with data mining algorithms such as Scikit-Learn, NumPy, SciPy and Pandas.
• Strong understanding of machine learning models, model training, and hyper parameter tuning.
• Working knowledge of deep learning models, loss functions, and accuracy measures.
• Hands-on proficiency with PyTorch / TensorFlow / Keras.
• Experience in NLP solutions preferred.
• Experience in parsing pdf and text documents preferred.
• Experience with statistical regression, neural nets, deep learning, decision trees, SVM, ensembles is expected.
• Multi-Cloud experience and proficiency with providers AWS, GCP or Azure.
• Comfortable working in a micro services environment.
• Self-motivated, enthusiasm to build next generation AI systems.

Roles &

Responsibilities :

• Analyze product requirements and come up with data science solutions.
• Identify and develop data engineering scripts (example : parsers) necessary to build training datasets.
• Build deep learning NLP model(s), customize as needed to meet the requirements.
• Write production quality python code for model development and as well as for inference.
• Ability to think out-of-the-box and implement custom loss functions and quantitative methods to increase accuracy of AI solutions.
• Build and ship AI agentdriven systems that reason, plan, and act across real-world, messy data.
• Design agentic workflows using LLMs, tools, retrieval, and memory to solve high-impact product problems.
• Work hands-on with unstructured and multimodal data (text, PDFs, drawings, images, logs).
• Develop multimodal models (text vision) for document understanding and contextual reasoning.
• Own AI solutions end-to-end : data pipelines, modeling, deployment, monitoring, and iteration.
• Write production-grade Python code and rapidly prototype, test, and ship in a cloud-native environment.
• Take complete ownership of the solution in all phases - analysis, proof of concept, data engineering, model development, model tuning, and model implementation.