Solution Architect Data Modernization, GenAI

Solution Architect Data Modernization, GenAI

Location- Bangalore ( Hybrid )

Role- Full Time

About Kearney Activate

Kearney Activate is the business acceleration and enablement arm of Kearney Management Consulting, helping clients accelerate transformation through Data and AI. Our Data & AI business line bridges strategy, technology, and execution to deliver data-driven, AI-enabled, and cloudpowered transformations. We partner with leading platforms such as Microsoft, AWS, Snowflake, Databricks and Nvidia to help organizations build trusted data foundations, deploy intelligent solutions, and scale innovation.

We operate across four key focus areas:

• Advisory & Assurance Services: Driving strategy, governance, and change management to ensure impactful transformation.
• Data Services: Building trusted, high-quality data foundations using Agentic AI across engineering, architecture,and governance.
• Insights Services: Unlocking value through analytics, data science, and AI-driven storytelling for better decisionmaking.
• Asset Services: Developing reusable GenAI assets, accelerators, and AI-powered solutions that push the boundaries of innovation.

About the Role

We are seeking a visionary AI-Enabled Solution Architect to help lead the regional design and delivery of next-generation, AI-native data and analytics solutions. This role is central to our transformation team as we embed Generative AI,

Agentic Intelligence, and modern data engineering into the core of how we operate and deliver value to our clients.

Youll not only shape cutting-edge architectures but also lead and inspire multidisciplinary teams, helping to develop the next generation of technical leaders who carry our depth of expertise into complex client environments. You'll operate at the intersection of technology and business, translating emerging capabilities into practical, high-impact solutions.

Key Responsibilities

• Architect scalable, modular platforms that integrate modern data engineering with GenAI and Agentic AI capabilities.
• Design and build end-to-end ingestion, transformation, and classification pipelines across structured and semistructured data sources (e.g., Excel, JSON).
• Implement RAG (Retrieval-Augmented Generation) pipelines using intelligent chunking strategies and multimodal retrieval (e.g., BM25, dense vector search, graph-based, structured data, and vision-based inputs).
• Drive data quality through embedded validation, monitoring, and automated reporting frameworks.
• Enable in-context learning and enterprise-scale agent design using different prompting techniques (like ReAct, Tree-of-Thoughts, Reflexion, or hybrid reasoning patterns) for automation, insight generation, and decision support.
• Lead exploratory data analysis (EDA), dynamic source classification, and ML model integration for advanced analytical and operational use cases.
• Mentor and grow a high-performing team of architects, engineers, and analystsfostering innovation, crossfunctional collaboration, and delivery excellence.
• Champion AI and data architecture as strategic enablers of business transformationlinking technology decisions directly to measurable client outcomes.

Technologies & Concepts

• Programming & APIs: PySpark, Python, FastAPI, FastMCP, SQL, OAuth2
• GenAI & Agentic Tools: RAGAS, Langfuse, LangGraph, Llamaindex, Vector Databases (FAISS, PGVector, Weaviate, AWS Bedrock, Azure AI Search)
• AI Architectures: Retrieval-Augmented Generation, In-Context Learning, Agent Design, Function Calling, LLM Fine-Tuning & Adaptation Strategies
• Data Engineering: Real-time ingestion pipelines, transformation logic, schema evolution, semi-structured data workflows
• Data Analytics & Quality: Exploratory Data Analysis, advanced classification, data quality reporting and observability
• Enterprise Integration: Secure API architecture, user feedback loops, instrumentation with Langfuse for traceability, MLOps and LLMOps frameworks to operationalise and govern ML/LLM pipelines across environments

Ideal Background

• Proven experience designing and scaling GenAI-enabled and data-driven architectures.
• Strong grounding in software engineering principles and AI/ML enablement.
• Demonstrated ability to lead, mentor, and grow cross-functional technical teams.
• Track record of delivering innovative, scalable solutions that drive clear business value.
• Passion for shaping intelligent systems and applying emerging technologies to solve real-world challenges.