AI Engineer + Java

About the Role

Your mission is to deploy AI agent chains to extract, analyse, and understand legacy estates at depth—then drive forward engineering onto a modern Spring Boot / Java 21 / Angular / MongoDB cloud-native stack. AI agents will accelerate design, code generation, test authoring, and migration validation at every step.

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 to 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 to 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, 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 agentic AI platforms and frameworks: Claude Code CLI (Anthropic), Cursor, Gemini CLI (Google), LangChain / LangGraph, AutoGen / CrewAI, and Anthropic Agent SDK / OpenAI Assistants API.
• 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.

Requirements

• Bachelor of Computer Science (or equivalent).

Skills

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.

Benefits

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

Pay

Salary Range: $120,000 – $160,000 a year.