Data Scientist/Technical Lead - LLM/RAG

Core Responsibilities : (A) Data Science Responsibility : Design, Build & Manage Lifecycle of AI Systems : - Multi-Agent Orchestration : Design and implement multi-agent workflows for AI functionalities : Intent Detection, High-Quality Response Generation, Logic Audit, Query Clarification, Auto-Visualization, Code Generation, Syntax-Validation, etc. - Intelligence Enhancement : Build a Human-in-the-Loop (HITL) feedback flywheel to continuously improve the golden data store, model accuracy, and response quality. - Semantic Layer Mastery : Construct a rich Data Dictionary to define complex business terms and metrics. Ensure the AI speaks the language of our customers and business stakeholders warm, professional, and easy to understand. - AI Lifecycle Mgmt & Full-Stack AI Execution : Own major portions of the AI Roadmap. Deploy LLMs and perform code execution via cloud services. Ensure high-quality, production-ready AI code throughout the entire lifecycle. - Data Engineering, Governance & Observability : Build Data Engineering Pipelines. Ensure every AI-generated SQL/Code is analysed and evaluated before and after execution. Maintain strict data quality, sanity, reliability standards, and semantic accuracy. Manage Data Lifecycle and Traceability. (B) Tech Leading Responsibility : Lead, Mentor & Guide the Team : - Team Mentoring & Guidance : Actively mentor and guide junior and mid-level engineers. Help them upskill in AI/ML, best practices, and sharp problem-solving. Be the go-to person for technical challenges across the AI/ML layer and beyond. - Code Reviews & PR Approvals : Review code and approve Pull Requests. Manage merge requests and administrate code-repo branches. Be the proponent and custodian of code quality, design standards, and engineering best practices. - Technical Unblocker & Force Multiplier : Proactively identify and remove team blockers. Guide the team through complex debugging, tough architectural decisions, and unfamiliar technology choices. Be the last escalation point before the CTO. - Product & Application Understanding : Maintain a deep, holistic understanding of the overall product, application architecture, and business context beyond just the AI/DS layer to make well-informed trade-off and prioritisation calls. - Hands-On Development : Contribute directly to application and product development as and when required whether its building a feature, fixing a critical bug, or accelerating a sprint. This is in addition to the Data Science responsibilities. - Engineering Culture : Be the steward of a high-performance, high-integrity engineering culture. Promote accountability, psychological safety, fast iteration, and a zero-compromise attitude towards code quality and system design. Roles Requirements : The Intersection of Data Rigor and AI/GenAI Innovation We are looking for a world-class AI specialist who is part Cloud AI Practitioner, part Data Science (DS) innovator, and part MLOps ninja. Essentially, a fullstack AI expert. [MH] Must-Have Skills & Experience : - AI/ML Core : 2+ years of experience with Python, AI libraries, GIT, GitHub, production grade AI/ML models & systems. 2+ years developing innovative AI & DS features. - GenAI & RAG : Experience building high quality Retrieval-Augmented Generation (RAG) architectures, Knowledge-Bases (KBs), & AI-Agents, using LLMs. - Data Engineering (DE) : Deep proficiency in SQL and PostgreSQL, and vector databases for semantic search. Experience with vector embeddings and ETL. - Cloud AI Practitioner : Hands-on experience with AWS Bedrock, Lambda, RDS, Sagemaker, AI-Agent frameworks and other AI Services in AWS/GCP/Azure. - AI Tooling Expertise : Expert-level usage of Cursor, Claude Code, Gemini, or Replit to automate design, code generation, debugging, testing and deployment. [SH] Should-Have Skills & Experience : - Conversational BI : Experience building AI Assistants providing instant customized insights in response to natural language queries, using LLMs like Claude or GPT. - Orchestration Frameworks : Experience with AgentCore, LangGraph, N8N, or CrewAI, for multi agent pipelines/workflows. Exp with Linux & open-source tools. - Financial/Fintech/AI Rigor : Can handle complex financial metrics & ensure data sanity & quality. Ability to understand/improve model quality & monitor for model drift. - MLOps & AI Governance : CI/CD for LLMs, LLMOps, Prompt Versioning, AI/ML Models Selection/Training/Fine-Tuning/Testing/Monitoring, Robust SCM practices, Agile/Scrum, AI Reliability, Security, AI Performance & Cost Optimization, etc. [GTH] Good-To-Have Skills & Experience : - Educational Background : Degree in Computer Science or Engineering or Data Science or AI/ML from a premier institution (IITs/NITs/BITS/IIIT) or a top university. - Frontend & Backend Experience : Vibe-coding Experience, Visualizations (using FusionCharts, Charts.js, etc.), FastAPI (APIs), etc., to give AI Assistant POC demos. Core Resp