AI Engineer + Java

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/FunctionalSkills## The Modernisation MissionLegacy estates in scope typically include:Mainframe COBOL/CICS/IMS batch and online transaction processingHierarchical and relational databases (IMS, DB2) with deeply embedded business logicProprietary messaging middleware (IBM MQ) and brittle point-to-point integrationsLegacy OO platforms (VisualAge Smalltalk, Tonel format) with no test coverage or documentationJCL/Assembler job streams woven into business-critical workflowsYour 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 EngineeringDesign 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 graphsBuild 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 casesUse agents to auto-generate legacy comprehension artefacts: annotated COBOL walkthroughs, IMS segment relationship diagrams, CICS program call trees, and DB2-to-document data-model mappingsOrchestrate agent workflows that identify dead code, duplicated logic, and tightly coupled components - producing prioritised decomposition candidates for the modernisation backlogValidate agent-extracted business rules against domain SMEs; build feedback loops that improve agent accuracy over successive extraction cycles• ### AI-Augmented Forward EngineeringDesign 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 standardsBuild agents that enforce architectural patterns during code generation: no business logic in adapters, domain models free of persistence concerns, API contracts decoupled from internal representationsDeploy agents for migration validation - automatically comparing migrated service behaviour against legacy outputs across a curated test corpus, flagging behavioural divergence before human reviewUse 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 themChain 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 & EngineeringArchitect 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 loopsCursor - AI-native IDE agent workflows, codebase-wide context, rule-based agent behaviourGemini CLI (Google) - Gemini-powered agent pipelines with tool use and long-context reasoningLangChain / LangGraph - chain and graph-based agent orchestration, tool registries, state machinesAutoGen / CrewAI - multi-agent conversation frameworks, role-based agent specialisationAnthropic Agent SDK / OpenAI Assistants API - programmatic agent construction with tool use, memory, and structured outputSelect the right orchestration pattern for each workstream: sequential chains, parallel fan-out, supervisor/worker, reflection loops, human-in-the-loop checkpointsBuild domain-specific agent tools: legacy code readers, schema extractors, API contract validators, test harness runners, cloud cost estimators, IaC generatorsDesign human-in-the-loop checkpoints: define what agents decide autonomously, what they flag for engineer review, and what requires architect sign-offEvaluate, benchmark, and improve agent chain quality: extraction completeness, forward-engineering accuracy, false-positive rates, and time-to-output• ### Solution Design & Technical AuthorityOwn 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 designsEvaluate 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 rationaleDefine integration patterns for hybrid-state environments: mainframe co-existence, MQ-to-event-streaming migration, dual-write data consistency, feature-flag-controlled cutoversLead design reviews; drive alignment between AI workstream leads, legacy SMEs, domain engineers, and cloud platform teams• ### Technical Leadership & Team DevelopmentLead a cross-functional team spanning backend, frontend, data migration, and AI/agent engineeringConduct structured code reviews across both hand-authored and agent-generated code - human review of AI output is non-negotiable; agents accelerate, engineers ownEstablish standards for agent-assisted development: what must be reviewed, what must be tested, how agent outputs are versioned and auditedMentor engineers on agentic AI patterns, prompt engineering for code tasks, and responsible use of AI-generated artefacts in production systemsCoach engineers unfamiliar with legacy systems to read COBOL/IMS structures via agent-assisted comprehension tools you have built• ### Delivery ExecutionBreak modernization epics into sprint-deliverable stories with measurable progress indicators: % business logic migrated, legacy endpoints retired, agent pipeline accuracy metricsTrack and communicate migration coverage - human-readable progress dashboards built partly by agents, owned by youIdentify and mitigate transition risks: agent hallucination in business rule extraction, data consistency during dual-write phases, performance parity of migrated servicesOwn sprint-level commitments; surface blockers with proposed mitigations, not status updates