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.