Solutions Architect

Job Description: Solution Designer – AI-Led Mainframe Modernization
Experience: 5–7 Years
Location: Chennai / Noida / Pune / Hyderabad
Domain: Banking & Financial Services | Generative AI | Cloud Modernization | Data Architecture
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About Intellect Design Arena
Intellect Design Arena is a global leader in Financial Technology, serving over 270+ banking and financial institutions across 57 countries. Powered by our First Principles Thinking and Design Thinking methodology, we build composable, cloud-native, and AI-first solutions across Corporate Banking , Retail Banking, Treasury and Insurance & AI.
Role Overview
We are looking for a high-performing Solution Designer to join our Core Modernization Practice. In this role, you will lead high-impact Mainframe Exit and Legacy Modernization programs for tier-1 global banks.
By integrating cloud-native architecture, enterprise Data Lakehouses, and Generative AI / RAG frameworks, you will design target-state architectures that extract business rules from legacy COBOL/JCL systems, automate data migration with zero data loss, and deliver scalable, auditable platform transformations.
Key Responsibilities
Architecture & Cloud Modernization
Design end-to-end cloud-native modernization solutions for core banking, treasury, and transaction banking platforms on AWS, Azure, Snowflake, and Databricks.
Architect enterprise data models, multi-tenant Data Lakehouses, and high-throughput ELT/ETL pipelines for seamless mainframe data extraction and streaming.
Optimize large-scale data processing performance using Snowpark, Delta Lake, PySpark, and AWS/Azure native services.
AI-Powered Transformation Frameworks
Design and implement AI solution accelerators utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to decipher legacy codebases (COBOL, JCL, VSAM), automate business-rule extraction, and accelerate discovery phases.
Build enterprise AI knowledge assistants that enable domain architects and business analysts to query legacy and modernized target-state data models using natural language.
Data Integrity & Governance
Lead enterprise-grade data migration streams with robust automated reconciliation engines using row-count validation, checksum verification, and complex business-rule reconciliation to achieve 100% data fidelity.
Enforce design integrity, security protocols, audit trails, and data governance across multi-cloud environments in compliance with global financial regulations (e.g., GDPR, RBI, OCC guidelines).
Delivery & Stakeholder Collaboration
Collaborate with bank CTOs, enterprise architects, and product engineering teams to translate complex legacy workflows into composable microservices and cloud event streams.
Define technical delivery roadmaps, solution architecture blueprints, and engineering standards for cross-functional agile teams.
Technical & Functional Skills
Category
Skills & Technologies
Cloud & Data Platforms
Snowflake, AWS (Glue, S3, Lambda, Redshift, QuickSight), Azure (Data Factory, Synapse, Analysis Services), Databricks
Programming & Processing
Python, Advanced SQL, PySpark, Snowpark
Data Architecture
Enterprise Data Modeling, Lakehouse Design, Delta Lake, High-Volume ELT/ETL, Mainframe Data Extraction (VSAM, DB2, EBCDIC decoding)
AI & Analytics
LLMs, RAG Architectures, Vector Databases, LangChain/LlamaIndex, Power BI, Tableau, AWS QuickSight
Data Quality & Reconciliation
Automated Reconciliation Frameworks, Checksum Verification, Business Rule Validation
Preferred Experience
Direct experience in migrating high-volume financial data streams (Payments, Core Banking, Treasury, or Trade Finance) off legacy Mainframes.
Proven track record of applying Generative AI/LLMs to enterprise engineering problems (e.g., COBOL-to-Java translation analysis, automated documentation).
Hands-on involvement in multi-tenant, cloud-native deployments for global financial institutions.
What Success Looks Like at Intellect
Acceleration: Shorten discovery and legacy code analysis timelines by 30–40% through AI/RAG-driven accelerators.
Fidelity: Guarantee 100% data accuracy and zero financial drift during phase-by-phase cutovers using automated reconciliation frameworks.
Performance: Deliver target-state architectures that improve query performance, lower processing latency, and optimize cloud consumption costs for client banks

Work arrangement
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