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# AI Engineer + Java

- **Company:** ClifyX, INC
- **Location:** Readington Township, NJ, United States
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Business Logic, Architectural Patterns, Customer Information Control System (CICS), Cloud Computing, COBOL (Programming Language), Code Coverage, Code Generation, Code Review, Continuous Integration, Data Migration, IBM DB2, Relational Databases, Cursor, Document-Oriented Databases, Middleware, Data Flow Control, Job Control Language (JCL), IBM WebSphere MQ, Junit, Mainframes, MongoDB, Online Transaction Processing, Operational Databases, OpenAI, Reverse Engineering, Software Engineering, Systems Integration, Openapi, Design of Telemetry/logging Agents, Cloud Platform System, Claude Code, Multi-Agent Systems, Prompt Engineering, Spring-boot, State Machines, Backend, Agentic-AI, AngularJS, CrewAI, AutoGen, Front End Software Development, Smalltalk, Virtual Agents, Google Gemini, Software Coding, Terraform, Agent SDK, Api Management, Human in the Loop, Legacy Systems
- **Published:** November 6, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-java-whitehouse-station-nj--6c9fb944-99b6-4650-b5a3-9e4eae5f2a1c

## About the Role

Artificial Intelligence (AI), Software Engineering, Java, Java, Software Engineering, MongoDB, AngularJS, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Cloud Computing

## Description

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., " ### 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