AI agent chains** to extract, analyse, and understand this estate at depth then drive forward engineering onto a modern Spring Boot / Java 21 / Angular / MongoDB cloud-native stack, with AI agents accelerating design, code generation, test authoring, and migration validation at every step.
" Must Have Technical/Functional Skills
" ## The Modernisation Mission
" Legacy estates in scope typically include:
- Mainframe COBOL/CICS/IMS batch and online transaction processing
- Hierarchical and relational databases (IMS, DB2) with deeply embedded business logic
- Proprietary messaging middleware (IBM MQ) and brittle point-to-point integrations
- Legacy OO platforms (VisualAge Smalltalk, Tonel format) with no test coverage or documentation
- JCL/Assembler job streams woven into business-critical workflows
" Your mission: deploy AI agent chains to extract, analyse, and understand this estate at depth then drive forward engineering onto a modern Spring Boot / Java 21 / Angular / MongoDB cloud-native stack, with AI agents accelerating design, code generation, test authoring, and migration validation at every step.
" ## Key Responsibilities
" ### AI-Augmented Reverse Engineering
- Design and deploy custom AI agent pipelines that ingest legacy artefacts COBOL programs, IMS DBDs/PSBs, DB2 schemas, JCL, Smalltalk Tonel sources and produce structured outputs: business rule inventories, data-flow maps, domain entity models, and dependency graphs
- Build multi-agent chains that cross-reference extracted business logic against live transaction traces, test outputs, and production data patterns to validate completeness and surface hidden edge cases
- Use agents to auto-generate legacy comprehension artefacts: annotated COBOL walkthroughs, IMS segment relationship diagrams, CICS program call trees, and DB2-to-document data-model mappings
- Orchestrate agent workflows that identify dead code, duplicated logic, and tightly coupled components producing prioritised decomposition candidates for the modernisation backlog
- Validate agent-extracted business rules against domain SMEs; build feedback loops that improve agent accuracy over successive extraction cycles
" ### AI-Augmented Forward Engineering
- Design forward engineering agent chains that consume reverse-engineered domain models and produce: Spring Boot service skeletons, OpenAPI 3.1 contracts, MongoDB schema designs, Angular component scaffolds, and JUnit 5 test suites all aligned to team coding standards
- Build agents that enforce architectural patterns during code generation: no business logic in adapters, domain models free of persistence concerns, API contracts decoupled from internal representations
- Deploy agents for migration validation automatically comparing migrated service behaviour against legacy outputs across a curated test corpus, flagging behavioural divergence before human review
- Use AI to accelerate CI/CD pipeline authoring, infrastructure-as-code generation (Terraform, Helm), and runbook drafting with engineers reviewing and owning the outputs, not rubber-stamping them
- Chain agents to continuously scan modernised code for legacy anti-patterns bleeding into new services, enforce non-functional requirements (observability hooks, circuit breakers, health endpoints), and flag design drift from approved blueprints
" ### Custom Agent Design & Engineering
" Architect multi-agent systems using one or more agentic AI platforms and frameworks:
- Claude Code CLI (Anthropic) agentic coding, slash commands, MCP tool integration, custom agent loops
- Cursor AI-native IDE agent workflows, codebase-wide context, rule-based agent behaviour
- Gemini CLI (Google) Gemini-powered agent pipelines with tool use and long-context reasoning
- LangChain / LangGraph chain and graph-based agent orchestration, tool registries, state machines
- AutoGen / CrewAI multi-agent conversation frameworks, role-based agent specialisation
- Anthropic Agent SDK / OpenAI Assistants API programmatic agent construction with tool use, memory, and structured output
- Select the right orchestration pattern for each workstream: sequential chains, parallel fan-out, supervisor/worker, reflection loops, human-in-the-loop checkpoints
- Build domain-specific agent tools: legacy code readers, schema extractors, API contract validators, test harness runners, cloud cost estimators, IaC generators
- Design human-in-the-loop checkpoints: define what agents decide autonomously, what they flag for engineer review, and what requires architect sign-off
- Evaluate, benchmark, and improve agent chain quality: extraction completeness, forward-engineering accuracy, false-positive rates, and time-to-output
" ### Solution Design & Technical Authority
- Own end-to-end solution design for modernisation workstreams producing LLD documents, sequence diagrams, PlantUML/Mermaid data-model mappings, strangler-fig migration maps, and API surface designs
- Evaluate architectural trade-offs: lift-and-shift vs. re-platform vs. re-architect, agent-generated vs. hand-crafted, monolith decomposition sequencing all documented as ADRs with explicit rationale
- Define integration patterns for hybrid-state environments: mainframe co-existence, MQ-to-event-streaming migration, dual-write data consistency, feature-flag-controlled cutovers
- Lead design reviews; drive alignment between AI workstream leads, legacy SMEs, domain engineers, and cloud platform teams
" ### Technical Leadership & Team Development
- Lead a cross-functional team spanning backend, frontend, data migration, and AI/agent engineering
- Conduct structured code reviews across both hand-authored and agent-generated code human review of AI output is non-negotiable; agents accelerate, engineers own
- Establish standards for agent-assisted development: what must be reviewed, what must be tested, how agent outputs are versioned and audited
- Mentor engineers on agentic AI patterns, prompt engineering for code tasks, and responsible use of AI-generated artefacts in production systems
- Coach engineers unfamiliar with legacy systems to read COBOL/IMS structures via agent-assisted comprehension tools you have built
" ### Delivery Execution
- Break modernization epics into sprint-deliverable stories with measurable progress indicators: % business logic migrated, legacy endpoints retired, agent pipeline accuracy metrics
- Track and communicate migration coverage human-readable progress dashboards built partly by agents, owned by you
- Identify and mitigate transition risks: agent hallucination in business rule extraction, data consistency during dual-write phases, performance parity of migrated services
- Own sprint-level commitments; surface blockers with proposed mitigations, not status updates