We are seeking a highly skilled and experienced Data Scientist/Senior Data Scientist to join our dynamic team. In this role, you will design and build scalable machine learning and AI solutions, taking complete ownership of use cases from ideation through production deployment and monitoring. This position requires strong hands-on expertise in Python and PySpark, as well as a deep understanding of modern AI/ML techniques including Generative AI and Large Language Models.

Responsibilities:

- Design, develop, and deploy scalable machine learning, deep learning, and NLP models for real-world business problems.

- Build and operationalize GenAI and LLM-based solutions using frameworks like Langchain, LangGraph, CrewAI, and others

- Should have an understanding on how to incorporate MCP and A2A in the multi agentic framework.

- Conduct end-to-end project execution, including problem understanding, data analysis, feature engineering, modeling, evaluation, and deployment.

- Develop robust, production-ready code using Python and PySpark.

- Apply time series forecasting methods to build predictive systems for business planning and operations.

- Collaborate with cross-functional teams to understand requirements, align solutions, and communicate insights effectively.

- Deploy ML models in production environments and ensure their ongoing monitoring, performance tracking, and tuning.

- Own and manage use cases throughout their lifecycle, ensuring they deliver measurable value.

- Communicate results clearly to both technical and non-technical stakeholders.

Requirements:

- 5 to 9 years of experience in Data Science or Machine Learning roles.

- Strong Knowledge of Python, PySpark, SQL, Flask, Streamlet for large-scale data processing and production-level development.

- Should be able to create end to end ETL pipelines to feed the data to the deployed modules with excellent debugging skills to identify the potential issues whenever it breaks

- Familiarity with data frameworks such as Hadoop, databases (PostgreSQL, MongoDB, YugaByte), understanding of cloud-native deployments and infrastructure

- Strong math / analytical skills (e.g. statistics, algebra)

- Proficient in ML/DL frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

- Experience and deep knowledge in Generative AI, LLMs, Langchain, LangGraph, MCP, and A2A

- Strong experience in NLP, Deep Learning, and Time Series Forecasting techniques.

- Knowledge of ML model deployment tools and practices (e.g., Docker, MLflow, FastAPI).

- Hands-on experience in model performance monitoring and optimization in production.

- Logical problem-solving skills and a structured approach to solution design.

- Strong verbal and written communication skills with the ability to work directly with business users.

Preferred Qualifications:

- Experience with cloud platforms (e.g., AWS, GCP, Azure) for ML deployment.

- Familiarity with CI/CD pipelines for ML models.

- Exposure to vector databases, embeddings, and RAG-based architectures.