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AI Solution Architect – Agentic AI & Engineering Transformation at Ness, Bangalore

AI Solution Architect – Agentic AI & Engineering Transformation at Ness in Bangalore. Apply with swipeandhired.

At Ness, people come first. Here, you'll be part of a vibrant team that values curiosity, innovation, and growth. We work with industry-leading clients on projects that truly make an impact while supporting every team member in carving out their unique career path. With resources for learning, certifications, and hands-on experiences, Ness offers you more than just a job—it’s a place where your ideas, ambition, and well-being matter.

The 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 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)

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: 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?