About the role
Responsibilities:
- Advanced Data Analysis: Extract, clean, and analyze complex datasets to uncover actionable insights, trends, and business opportunities.
- Python-Driven Automation: Design, develop, and deploy robust Python scripts to automate repetitive data tasks, ETL pipelines, and reporting workflows.
- Agentic AI Implementation: Build and integrate autonomous AI agents (using frameworks like LangChain, LlamaIndex, or AutoGen) capable of querying databases, interpreting data context, and generating analytical summaries with minimal human intervention.
- Workflow Optimization: Identify bottlenecks in current data operations and implement intelligent automation solutions to improve efficiency, accuracy, and scalability.
- Cross-Functional Collaboration: Partner with business stakeholders, data engineers, and product teams to translate complex business requirements into automated data solutions.
- Dashboarding and Visualization: Create intuitive, real-time dashboards (using tools like Tableau, Power BI, or Python-based libraries) that interface seamlessly with AI-generated insights.
Requirements:
- Extensive Python Expertise: Master-level proficiency in Python and its data ecosystem (Pandas, NumPy, SciPy, Scikit-learn).
- Agentic AI and LLMs: Proven experience working with Large Language Models (OpenAI, Claude, open-source models) and agentic frameworks (LangChain, AutoGPT, LlamaIndex) to build autonomous data-reasoning agents.
- Automation Experience: Strong background in automating data workflows, utilizing tools like Apache Airflow, cron jobs, Selenium, or specialized RPA tools.
- Database and SQL Mastery: Advanced ability to write complex SQL queries and manage relational/non-relational databases (PostgreSQL, MySQL, MongoDB, etc. ).
- Data Storytelling: Strong ability to communicate complex, AI-driven insights clearly to both technical and non-technical stakeholders.
- Experience with Cloud Platforms (AWS, GCP, or Azure) and deploying Python/AI applications in cloud environments.
- Familiarity with vector databases (Pinecone, Weaviate, Milvus) for RAG (Retrieval-Augmented Generation) applications.
- Understanding of machine learning deployment and MLOps principles.