Key Responsibilities:
Problem Formulation & Business Alignment
- Translate complex business problems into structured data science and AI problems.
- Validate OKRs using statistical and analytical methods.
- Apply design thinking to develop scalable AI solutions.
AI & LLM Solution Development
- Design, develop, and fine-tune LLM-based applications.
- Implement Agentic AI frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Build and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise knowledge systems.
- Design prompts, tools, and orchestration layers for AI systems.
Autonomous AI Systems Development
- Develop systems capable of autonomous task execution across enterprise platforms (CRM, ERP, cloud systems).
- Enable AI-driven decision-making based on structured and unstructured data.
- Integrate APIs, databases, and enterprise tools for real-time automation.
Data Science & Analytics Leadership
- Lead data wrangling, feature engineering, and advanced analytics initiatives.
- Drive insight generation and data storytelling for executive decision-making.
- Optimize analytical processes and improve operational efficiency using data.
Decision-Making & Optimization
- Make technical and strategic decisions on model iterations and optimization cycles.
- Improve processes using real-time analytics and feedback loops.
Stakeholder & Customer Engagement
- Communicate insights and AI-driven recommendations to business stakeholders.
- Engage with users via chat, email, or voice-based systems (where applicable).
- Translate analytics outputs into actionable business insights.
Governance & Compliance
- Ensure ethical, legal, and organizational compliance in AI systems.
- Maintain governance standards for AI-driven automation systems.
