AI Solution Architect – Agentic AI & Engineering Transformation

Life at Ness

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:

1. 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

       2. 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

       3. Preferred exposure to below AI platforms/framework:

• LangGraph 
• AutoGen
• LangChain
• CrewAI
• Microsoft Copilot Studio
• Azure AI Foundry

     4. Software Engineering: Strong understanding of:

• Agile delivery
• Microservices
• Event-driven architecture
• SDLC methodologies

     5. 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":

1.      HR Interview – A conversation to get to know you better and align on expectations.

2.      Technical Interview – A chance to showcase your skills and experience.

3.      Client Interview – Ensuring mutual fit between you and the client team.

4.      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?