Talent500 is hiring for one of its clients.

Role Overview :

We are seeking a versatile and forward-thinking Data & AI Analyst to join our team. This role bridges the gap between traditional data analytics and up-to-date Generative AI implementation. You will be responsible for parsing highly unstructured documents (including complex Excel workbooks), leveraging Advanced Language Models (LLMs) to drive automation and insights and handling large-scale datasets,

We value strong, adaptable engineering fundamentals and problem-solving skills over platform-specific certifications.

Key Responsibilities

- Unstructured Document Parsing: Extract clean, structured insights from a wide variety of complex files—including PDFs, multi-tab Excel workbooks, financial models, PDFs, images, and scanned forms—with varying levels of data quality and layout consistency.
- Tool Selection & Strategy: Exercise sharp technical judgment to evaluate business problems and select the optimal technology mix—knowing when to use standard data manipulation libraries, a cloud OCR tool, or a heavyweight LLM.
- Business Application Integration: Help bridge the gap between standalone AI models and our core business applications, ensuring extracted data and AI insights flow smoothly into internal workflows and systems.
- Automated Report Writing: Design and implement systems that automate the creation of data-driven reports, summaries, and executive briefs, combining structured data analytics with GenAI text generation.
- Data Management & Processing: Query, transform, and manage large datasets within cloud data warehouses. Work seamlessly with optimized data storage formats (e.g., Parquet) to ensure high-performance data pipelines.
- AI & RAG Architecture: Design, build, and maintain Retrieval-Augmented Generation (RAG) systems to ground LLMs in internal company data, utilizing tools like Azure Document Intelligence for upstream data extraction.
- Collaboration & Code Hygiene: Maintain clean, documented, and reusable codebases. Participate in team workflows using standard Git version control practices.

Required Skills & Qualifications: Artificial Intelligence & Document Parsing:

- Advanced Document Extraction: Hands-on experience extracting data from messy, semi-structured, or completely unstructured files, with specific experience programmatically parsing or chunking complex Excel/spreadsheet data and PDFs.
- Extraction Tools:



Familiarity with Azure Document Intelligence or equivalent advanced layout extraction tools.
- RAG Frameworks: Practical experience building Retrieval-Augmented Generation systems (e.g., working with vector databases and frameworks like LangChain or LlamaIndex).
- Model Selection: Conceptual and practical familiarity with the AI ecosystem, including commercial APIs (OpenAI, Anthropic, Gemini) and lightweight/open-source models (Llama, Mistral, Phi).

Integration & Software Engineering:

- Systems Key Responsibilities
- Unstructured Document Parsing: Extract clean, structured insights from a wide variety of complex files—including PDFs, multi-tab Excel workbooks, financial models, PDFs, images, and scanned forms—with varying levels of data quality and layout consistency.
- Tool Selection & Strategy: Exercise sharp technical judgment to evaluate business problems and select the optimal technology mix—knowing when to use standard data manipulation libraries, a cloud OCR tool, or a heavyweight LLM.
- Business Application Integration: Help bridge the gap between standalone AI models and our core business applications, ensuring extracted data and AI insights flow smoothly into internal workflows and systems.
- Automated Report Writing: Design and implement systems that automate the creation of data-driven reports, summaries, and executive briefs, combining structured data analytics with GenAI text generation.
- Data Management & Processing: Query, transform, and manage large datasets within cloud data warehouses. Work seamlessly with optimized data storage formats (e.g., Parquet) to ensure high-performance data pipelines.
- AI & RAG Architecture: Design, build, and maintain Retrieval-Augmented Generation (RAG) systems to ground LLMs in internal company data, utilizing tools like Azure Document Intelligence for upstream data extraction.
- Collaboration & Code Hygiene: Maintain clean, documented, and reusable codebases. Participate in team workflows using standard Git version control practices.

Required Skills & Qualifications: Artificial Intelligence & Document Parsing:

- Advanced Document Extraction: Hands-on experience extracting data from messy, semi-structured,



or completely unstructured files, with specific experience programmatically parsing or chunking complex Excel/spreadsheet data and PDFs.
- Extraction Tools: Familiarity with Azure Document Intelligence or equivalent advanced layout extraction tools.
- RAG Frameworks: Practical experience building Retrieval-Augmented Generation systems (e.g., working with vector databases and frameworks like LangChain or LlamaIndex).
- Model Selection: Conceptual and practical familiarity with the AI ecosystem, including commercial APIs (OpenAI, Anthropic, Gemini) and lightweight/open-source models (Llama, Mistral, Phi).

Integration & Software Engineering:

- Systems Integration: Strong experience consuming and building RESTful APIs to connect AI models, data systems, and downstream business applications.
- Version Control: Proficient with Git and standard platform workflows (GitHub, GitLab, or Azure DevOps).

Data & Analytics Infrastructure:

- Big Data Handling: Comfortable working with large-scale datasets and optimized data formats (specifically JSON, CSV, and Parquet).
- Data Pipelines: Strong proficiency in manipulating data via Python and writing clean, efficient SQL.

Preferred / Nice-to-Have Qualifications:

- Experience working within the Azure cloud ecosystem and running queries in Snowflake .
- Automated Report Generation: Experience building systems that automatically synthesize data into natural language reports or automated client-facing summaries.
- Familiarity with prompt engineering techniques and LLM evaluation frameworks. n: Strong experience consuming and building RESTful APIs to connect AI models, data systems, and downstream business applications.
- Version Control: Proficient with Git and standard platform workflows (GitHub, GitLab, or Azure DevOps).

Data & Analytics Infrastructure:

- Big Data Handling: Comfortable working with large-scale datasets and optimized data formats (specifically JSON, CSV, and Parquet).
- Data Pipelines: Strong proficiency in manipulating data via Python and writing clean, effective SQL.

Preferred / Nice-to-Have Qualifications:

- Experience working within the Azure cloud ecosystem and running queries in Snowflake .
- Automated Report Generation: Experience building systems that automatically synthesize data into natural language reports or automated client-facing summaries.
- Familiarity with prompt engineering techniques and LLM evaluation frameworks.

📌 Data Scientist - AI-27599] (India)
🏢 Talent500
📍 India