Job highlights

- Identity Fraud: CIAM,authentication and fraud integrations . Batch Integration: APIs,MQ / middleware,batch and partner systems . Legacy Modernization: Java / WebSphere and cloud migration . Data platform Modernization: Migration from traditional warehouse to cloud(preferably Databricks) . Key Responsibilities
- Experience . 12+ years overall

Job Summary Role: AI Solution Architect - Agentic AI Engineering Transformation

Position Summary:

Seeking a highly experienced AI Architect to lead the design, adoption, and scaling of AI-driven Software Development Lifecycle (SDLC) practices and Agentic AI solutions across the organization.

Why this Role is important

This is not a traditional architecture role. You will be at the forefront of transforming a large-scale engineering organization into an AI-First Delivery Enterprise, defining how software is designed, built, tested, and operated in the age of Generative AI and Agentic AI.

You will have the opportunity to:

- Shape the organizations enterprise AI strategy and engineering future state.
- Build and scale Agentic AI capabilities from the ground up.
- Define next-generation AI-driven SDLC practices.
- Work on real-world AI transformation initiatives rather than isolated proofs of concept.
- Partner with senior architects, engineering leaders, business executives, and technology vendors.

Location

Offshore - India

Experience

12+ years overall; strong recent GenAI / Agentic AI architecture experience

Domain: Banking Financial Services - Cards, Payments, Customer Service, Collections, Identity, Loyalty, Digital Servicing

Role Purpose

Drive the CTFS AI transformation across the managed-services / vendor-consolidation portfolio,



focusing on AI-enabled SDLC, Agentic AI, application modernization, engineering productivity, operational automation and measurable savings.

CTFS Context

Work across

- DSS: Java-based digital servicing platform with ~55 microservices
- Pega: Customer service, servicing and disputes
- Collections: Delinquency/default servicing and recovery workflows
- Cards Payments: TSYS, payment gateway and transaction integrations

- Digital Channels: Mobile/web servicing
- Identity Fraud: CIAM, authentication and fraud integrations
- Batch Integration: APIs, MQ/middleware, batch and partner systems
- Legacy Modernization: Java/WebSphere and cloud migration
- Data platform Modernization: Migration from traditional warehouse to cloud(preferably Databricks)

Key Responsibilities

- Define AI target architecture and adoption roadmap
- Drive AI across requirements, design, coding, testing, documentation, release and support
- Design Agentic AI solutions using agents, MCP/API integrations and human-in-the-loop controls
- Establish reusable patterns for LLMs, RAG, agents, security, evaluation and observability
- Identify and prioritize AI use cases across DSS, Pega, Collections, Batch and Digital
- Apply AI to legacy analysis, modernization, testing and migration acceleration
- Enable AI-assisted incident triage, RCA,



knowledge retrieval and operational automation
- Drive PoC Pilot Production with security and governance controls
- Measure productivity improvement and translate it into validated capacity / cost savings
- Mentor engineering teams and create reusable accelerators
- Establish AI adoption frameworks, governance models, and implementation standards.
- Evaluate emerging AI technologies, frameworks, and platforms.
- Provide technical guidance to engineering teams, architects, and product owners.

Core Skills

Technical skills:

- AI Generative AI: Strong experience in:

- Large Language Models (LLMs)
- GPT-based architectures

- OpenAI ecosystem

- Azure OpenAI

- Anthropic Claude

- Gemini

- Prompt Engineering

- RAG architectures

- Vector databases

- Embedding models
- Fine-tuning concepts

- AI evaluation frameworks
- Agentic AI: Hands-on experience with:
- Multi-agent architectures

- Autonomous workflow agents

- AI orchestration frameworks

- Agent lifecycle management

- Planning and reasoning systems

- Tool integration frameworks
- Human-in-the-loop patterns

- MCP Server
- Preferred exposure to below AI platforms/framework:

- LangGraph

- AutoGen

- LangChain

- CrewAI

- Microsoft Copilot Studio

- Azure AI Foundry
- Software Engineering: Robust understanding of:

- Agile delivery

- Microservices
- Event-driven architecture

- SDLC methodologies

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Solution Architect - Agentic AI & Engineering Transformation (Bengaluru)
🏢 Ness Digital Engineering
📍 Bengaluru