Senior Technical Analyst (AI-Native)

Company Description

Ganguly & Company (GNCO) specializes in cutting-edge AI solutions, including advanced machine learning, deep learning applications, and custom large language models (LLMs). We focus on developing robust, scalable AI systems tailored to unique operational needs, spanning reinforcement learning and AI infrastructure. Partnering with GNCO means driving progress in automation and unlocking new possibilities for growth.

Role Description

We are looking for a Senior Technical Analyst who is \"AI-native\" by default. At GNCO, we don't just build AI; we live in it. You will be responsible for bridging the gap between high-level business goals and complex agentic AI systems. Unlike traditional analysts, you are expected to demonstrate advanced use of AI (LLMs, agents, and automated workflows) to perform your daily tasks. You won't just write requirements; you will build AI-driven pipelines to extract them.

Key Responsibilities

• AI-Driven Requirements: Utilize advanced prompting and NLP tools to transform raw stakeholder inputs and messy data into structured technical specs and user stories.
• Agentic System Design: Lead the functional design of complex AI agents , defining their personas, decision-making logic, tool-calling capabilities, and multi-agent orchestration.
• Product Lifecycle: Own the technical analysis for complex digital products, ensuring AI-native features like RAG pipelines and autonomous workflows are scalable and user-centric.
• Automated Documentation: Maintain an \"AI-first\" documentation stack, using AI to generate and audit technical manuals and system diagrams in real-time.
• Rapid Prototyping: Use AI-assisted coding environments (e.g., Claude, Gemini) to validate technical feasibility before full-scale engineering begins.

Qualifications

• Experience: 5+ years in Technical Analysis or Product Management, with a proven track record of delivering complex digital products .
• AI Literacy: Deep understanding of agentic logic, Retrieval-Augmented Generation (RAG), and how LLMs function (parameters, tokenization, etc.). 
• Technical Stack: Proficiency in Python and SQL is mandatory for interacting with AI APIs and data manipulation. 
• The AI-Native Mindset: Ability to demonstrate how you leverage AI to automate your own workflows and accelerate project delivery.