AI Engineer

a {
text-decoration: none;
color: #464feb;
}
tr th, tr td {
border: 1px solid #e6e6e6;
}
tr th {
background-color: #f5f5f5;
}

AI Data ScientistRole Overview
We are looking for an innovative AI Data Scientist to design and implement synthetic data solutions that power advanced AI workflows. This role involves creating high-fidelity, privacy-compliant datasets, applying generative modeling techniques, and collaborating with cross-functional teams to integrate data into GenAI applications.

Key Responsibilities

• Synthetic Data Generation: Design, generate, and validate synthetic datasets across multiple formats (tables, time-series, images, PDFs, JSON, Excel, CSV).
• Generative Modeling & Privacy: Apply techniques such as GANs, VAEs, statistical resampling, and data masking to simulate realistic, privacy-aware data.
• Data Enrichment & Classification: Clean, tag, classify, and enrich real or synthetic data to support risk detection, insights, and LLM-driven decision workflows.
• Research & Innovation: Explore emerging techniques for structured output extraction and data simulation; share findings, tools, and best practices with the team.
• Visualization & Reporting: Build dashboards and visualizations to summarize metrics, simulation results, and insights for stakeholders.
• Cross-Functional Collaboration: Work closely with AI Data Engineers and Frontend Developers to embed synthetic and structured data into GenAI applications and POCs.
• Compliance & Governance: Ensure all data usage adheres to privacy, regulatory, and compliance standards in partnership with legal and risk teams.

• Strong proficiency in Python and libraries such as SDGym, Synthpop, Faker, or Synthea.
• Expertise in pandas, NumPy, scikit-learn, and data wrangling for structured/unstructured data.
• Experience with Jupyter-based experimentation and visualization tools (matplotlib, seaborn, Plotly).
• Familiarity with structured output tooling (e.g., Pydantic, LangChain) and integration with LLM pipelines.
• Bonus: Hands-on experience with PyTorch or TensorFlow for custom generative models.
• Comfortable working in notebook environments (Jupyter, Databricks) for exploration and modeling.
• Knowledge of BI tools (Power BI, Streamlit, Dash) for stakeholder-facing dashboards is a plus.