AI Engineer + Java

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: - **Cl aude 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