AI Solution Architect I

Job Summary

Engagement-level architect who owns the technical solution for individual modernization workstreams. Translates legacy application analysis (often surfaced via AI Force AI Workbench) into target-state cloud-native architectures, working closely with client architects and the CN COE delivery team. Brings deep expertise in legacy estate assessment—spanning COBOL/mainframe, Java EE monoliths, .NET, and RPG applications—combined with the ability to design agentic AI pipelines and GenAI-native application architectures. This is the first true ‘architect’ rung and the entry point into the consulting solutioning track.

Key Responsibilities

Application Discovery & Current-State Assessment

• Lead application discovery and current-state assessments using GenAI-assisted code analysis and AI Force AI Workbench.
• Assess and classify legacy portfolios—COBOL/mainframe, Java EE monoliths, legacy .NET, RPG, and end-of-life frameworks—using AI-assisted portfolio intelligence tools.
• Conduct business rule extraction from legacy codebases using GenAI tooling, preserving institutional knowledge before modernization.
• Produce comprehensive application profiles: dependency maps, data flow diagrams, complexity scoring, and technical debt assessments.

• Target-State Architecture & Migration Planning

  • Author target-state architectures (containers, serverless, managed services, event mesh) with clear migration patterns: rehost, replatform, refactor, rearchitect, replace, and retain.
  • Design target-state architectures for post-modernization GenAI-native applications, incorporating RAG pipelines, LLM integration layers, and agent orchestration frameworks.
  • Co-author PRDs and BRDs alongside client product owners, using AI-generated artifacts as accelerators.
  • Define non-functional requirements: scalability, resilience, observability, security, and FinOps.
  • Partner with Platform Engineers to ensure the target architecture is buildable on the chosen landing zone.
  Present technical recommendations to client director-level stakeholders

• Legacy Application Modernization

  • Apply GenAI-assisted business rule extraction to preserve institutional knowledge from COBOL, RPG, and legacy Java systems before migration.
  • Design modernization journeys across the full spectrum: mainframe offload, COBOL-to-Java/cloud-native transpilation, monolith decomposition into microservices, and UI framework migration (e.g., Angular to React).
  • Leverage composable Agentic AI platforms such as ATLAS and AI Force 2.0 to accelerate modernization from discovery to production.
  • Define rollback playbooks and cutover strategies for high-risk legacy system cutovers.
  Champion the AI Force Legacy Modernization Recipes for repeatable, scalable delivery patterns

• Agentic AI & GenAI Application Modernization

  • Design and deploy agentic AI pipelines for automated code analysis, dependency mapping, transformation planning, and validation—reducing manual discovery effort by 60–70%.
  • Architect GenAI-native applications using patterns such as Retrieval-Augmented Generation (RAG), multi-agent orchestration, tool/function calling, and LLM fine-tuning.
  • Configure and govern multi-provider LLM integrations (Azure OpenAI, Google Vertex AI, AWS Bedrock) with appropriate routing, cost, and latency controls.
  • Define guardrails, evaluation frameworks, and observability stacks for GenAI-powered applications in production.
  • Integrate LLM-based code generation, semantic diff, and automated test generation into CI/CD pipelines to accelerate modernization velocity.
  Assess and mitigate AI-specific risks: hallucination, prompt injection, data leakage, and model drift for production GenAI deployments.

• SDLC & DevOps Transformation

  • Integrate GenAI tooling across the full SDLC: requirements, design, code generation, testing, security scanning, and documentation.
  Establish AI-augmented CI/CD pipelines that include automated regression, semantic diff, and migration validation gates
• Define branching strategies, migration-as-code patterns, and rollback playbooks for high-risk legacy cutover events.

Skill Requirements

Core Experience

• 6–9 years of software engineering and architecture experience, with at least 2 years in an architect role.
• Demonstrated success leading at least 2 application modernization or cloud migration engagements.
• Deep knowledge of at least one hyperscaler service catalog (AWS, Azure, or GCP).
• Comfort facilitating client workshops and producing professional architecture documentation.

• Legacy Application Modernization

  • Hands-on experience assessing and modernizing legacy systems: COBOL/mainframe, Java EE monoliths, legacy .NET, or RPG applications.
  • Familiarity with AI-led modernization platforms such as AI Force 2.0, GitHub Copilot, Amazon CodeWhisperer, or equivalent tooling.
  • Ability to perform or oversee business rule extraction from legacy codebases using GenAI-assisted analysis.
  • Experience applying the 6R/7R migration strategies (rehost, replatform, refactor, rearchitect, replace, retire, retain) to enterprise legacy portfolios.

  • Agentic AI & GenAI Application Architecture

    • Working understanding of GenAI patterns (RAG, agents, evaluation) and how they apply to SDLC acceleration and legacy modernization.
    • Proficiency with agentic AI frameworks—LangChain, LangGraph, AutoGen, CrewAI, or equivalent—and multi-agent orchestration patterns.
    • Strong understanding of GenAI application patterns: prompt engineering, LLM integration, semantic search, function/tool calling, and structured output.
    Awareness of responsible AI principles: hallucination mitigation, bias detection, data privacy, and governance in GenAI deployments

Other Requirements

Certifications & Frameworks

• Professional-level cloud certification (AWS SA Pro, Azure Solutions Architect Expert, GCP PCA).
• TOGAF or equivalent EA framework exposure.

Industry vertical depth: financial services, insurance, healthcare, or public sector

Advanced Modernization & AI Tooling

• Experience with composable Agentic AI modernization platforms (e.g., ATLAS, AI Force 2.0 AI Workbench).
• Multi-provider LLM configuration and governance: Azure OpenAI, Google Vertex AI, AWS Bedrock—including routing strategies, cost controls, and latency SLOs.

Demonstrated GenAI business rule extraction from COBOL, RPG, or legacy Java codebases 

• Knowledge of vector databases, embedding models, and semantic search architectures (e.g., Pinecone, Azure AI Search, pgvector).
• Experience building autonomous data engineering pipelines for legacy data modernization.

GTM & Evangelism Contribution

• Leads technical solutioning on RFPs and proposals; contributes target-state diagrams and assumptions to SOWs.
• Develops AI Force–based modernization POVs and demo storylines for client leadership engagements (e.g., COBOL business rule extraction demos, Agentic AI transformation workshops).
• Contributes to CN COE knowledge base: reusable modernization recipes, GenAI architecture reference patterns, and best-practice playbooks.
• Represents HCLTech at industry events and client briefings as a thought leader in AI-led legacy modernization and GenAI application architecture.