AI Solution Architect – Agentic AI & Engineering Transformation
Ness Digital Engineering
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Job summary
Ness Group is seeking an experienced AI Solution Architect to lead the design, adoption, and scaling of AI-driven SDLC practices and Agentic AI solutions across a large engineering organization. The role emphasizes transforming delivery into an AI-First Enterprise, with governance and measurable cost savings.
Location is Offshore – India. The ideal candidate has 12+ years of experience, strong GenAI/Agentic AI expertise, and exposure to financial services domains including cards, payments, and
Qualifications
- 12+ years of overall experience with strong GenAI/Agentic AI architecture experience.
- Experience in Financial Services domain (cards, payments, customer servicing, collections, identity, fraud).
- Leadership experience with ability to influence senior leadership and engineering organizations.
- Experience delivering AI-enabled SDLC transformations and modernization initiatives.
Responsibilities
- Define AI target architecture and adoption roadmap.
- Drive AI across requirements, design, coding, testing, documentation, release and support.
- Design Agentic AI solutions with 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 automation.
- Drive PoC → Pilot → Production with governance controls.
- Measure productivity improvements and translate into cost savings.
- Mentor engineering teams and create reusable accelerators.
Skills
Tools
Job description
Location: Bangalore, India, India
Date Created: Nov 3, 2026
Requisition ID: 9427
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.
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.
- 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
- 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:
- GPT-based architectures
- OpenAI ecosystem
- Anthropic Claude
- Gemini
- Prompt Engineering
- AI evaluation frameworks
2. Agentic AI: Hands-on experience with:
- Autonomous workflow agents
- AI orchestration frameworks
- Planning and reasoning systems
- Tool integration frameworks
- Human-in-the-loop patterns
- MCP Server
3. Preferred exposure to below AI platforms/framework:
- LangGraph
- AutoGen
- LangChain
- CrewAI
4. Software Engineering: Strong understanding of:
- Microservices
- Event-driven architecture
- SDLC methodologies
5. Cloud & Platforms: Experience with:
- 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
- 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
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
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