AI Engineer + Java

Job Description 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: ext…