AI Solution Architect – Agentic AI & Engineering Transformation

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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 organization's 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)

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: Strong understanding of:

Agile delivery

Microservices

Event-driven architecture

SDLC methodologies

• Cloud & Platforms: Experience with:

Microsoft Azure

AWS

Google Cloud

AI services and cloud-native architecture

Devops pipeline for both Brownfield and GreendField applications

Leadership Skills

Ability to influence senior leadership and engineering organizations

Executive stakeholder management

Team mentoring and coaching

Strong communication and presentation skills

Preferred Experience

Financial services, cards/payments, customer servicing, collections, fraud/identity, enterprise modernization, Pega, Java/Spring, NodeJS, MQ, Azure, batch processing and regulated environments.

Expected Outcome

AI Roadmap → Prioritized Use Cases → Production AI Solutions → SDLC/Productivity Improvement → Measurable Savings

Success Metrics: The AI Architect will be measured on:

AI adoption across engineering teams

SDLC productivity improvement

Reduction in development and testing effort

Number of AI-enabled delivery programs

Agentic AI implementation success rate

Reusable AI accelerators created

Architecture compliance and governance adherence

Engineering team AI maturity growth

What to Expect Next

We believe great experiences start with transparency—and that includes our hiring process. Here’s what you can typically expect after you hit "Apply":

• HR Interview – A conversation to get to know you better and align on expectations.
• Technical Interview – A chance to showcase your skills and experience.
• Client Interview – Ensuring mutual fit between you and the client team.
• Offer Stage – If everything aligns, we’ll be happy to make it official.

While many of our roles involve just 2–3 steps, some client processes may include additional conversations. Regardless of the path, we aim to keep every step transparent and respectful of your time.

Ready to Start Your Journey?