We are looking for a Technical Lead Generative AI to design and lead end-to-end GenAI solutions for reporting and analytics. The role requires strong expertise in Data Science, SQL, data architecture, and GenAI technologies such as Microsoft Copilot Studio and Databricks, with the ability to architect data flows, integrate enterprise data, and deliver production-ready AI-powered reporting applications. The candidate should possess excellent communication skills and be able to articulate solution architectures, technical trade-offs, and business value to senior stakeholders.

:: Technical Lead – Generative AI, Reporting & Analytics

Role: Technical Lead – Generative AI

Experience: 10–15 years overall, with 3+ years in Data Science, Advanced Analytics, AI/ML or Generative AI

Role Type: Technical Architecture, Solution Design & Implementation

Key Responsibilities

- GenAI Solution Architecture & Design

Define end-to-end architecture for GenAI-powered reporting, analytics, and business intelligence applications.

Design solutions enabling users to query enterprise data using natural language, generate insights, automate reporting, and interact with analytical outputs.

Architect solutions using Microsoft Copilot Studio, Databricks, LLMs, RAG, AI agents, and enterprise data platforms as appropriate.

Define integration patterns across data sources, analytical models, semantic layers, APIs, LLMs, and reporting interfaces.

- End-to-End Data Flow & Implementation

Design and document complete data flows, from source systems and ingestion pipelines through transformation, data modeling, retrieval,



orchestration, and end-user reporting.

Lead implementation of GenAI applications, including data preparation, prompt engineering, retrieval mechanisms, tool/function calling, and integration with enterprise systems.

Establish appropriate approaches for structured-data querying, SQL generation, semantic search, contextual retrieval, and grounded responses.

Ensure data quality, accuracy, traceability, security, scalability, performance, and cost efficiency across the solution lifecycle.

- Data Science & Analytics

Apply solid Data Science, BI, and analytical problem-solving skills to translate business questions into meaningful insights.

Understand data modeling, statistical analysis, SQL, Python, KPI frameworks, and analytical methodologies.

Design solutions that combine traditional analytics and predictive models with GenAI capabilities.

Ensure AI-generated insights and reports are reliable, explainable, and aligned with business definitions and metrics.4. Stakeholder Communication & Solutioning

Engage with senior business and technology stakeholders to understand requirements and shape solution approaches.

Explain complex architectures, data flows, dependencies, risks,



and technology choices in clear business-oriented language.

Create architecture diagrams, solution blueprints, technical proposals, and executive-level presentations.

Lead solution walkthroughs, technical discussions, and stakeholder presentations, connecting technical decisions to business outcomes.

Required Skills & Experience

Robust experience in Data Science, Data Analytics, Business Intelligence, or Data Engineering, with the ability to understand end-to-end analytical workflows.

Proven experience designing and implementing Generative AI applications for enterprise reporting, analytics, or decision support.

Hands-on experience with Microsoft Copilot Studio and/or Databricks, including their integration with enterprise data and AI services.

Strong understanding of LLMs, prompt engineering, RAG, AI agents, orchestration, tool calling, and evaluation techniques.

Proficiency in SQL and Python, with a solid understanding of data pipelines, transformations, data models, APIs, and cloud-based data platforms.

Experience designing end-to-end architectures covering data ingestion, processing, storage, semantic or retrieval layers, AI orchestration, and user-facing applications.

Ability to assess when to use traditional BI, deterministic SQL, predictive analytics, RAG, or agentic GenAI approaches.

Strong communication, presentation, documentation, and stakeholder management skills.

Demonstrated ability to lead technical implementation while maintaining a business-outcome-oriented approach.

📌 Gen AI Architect (Bengaluru)
🏢 Tenarai Technologies
📍 Bengaluru