Senior Technology Lead || Jersey City, NJ (Hybrid)

Job Role: Senior Technology Lead Agentic AI & Legacy Modernization

Location: Jersey City, NJ (Hybrid)

Duration: Contract for 06+ Months

Job Description:

• We are seeking a Senior Technology Lead who sits at the intersection of **agentic AI, legacy modernisation, and cloud-native engineering**. This is not a role about using AI as a productivity shortcut - it is about designing and deploying custom AI agent pipelines that systematically reverse-engineer legacy estates and forward-engineer modern cloud-native platforms at a pace and depth that human-only approaches cannot match.
• You will develop and operate AI-driven modernisation workflows, produce detailed solution designs, lead a cross-functional engineering team, and own delivery commitments end-to-end. You write the agents, you write the designs, you do the code reviews, and you set the engineering bar.

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:
  • **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

Required Skills & Experience

• Agentic AI Engineering (Must-Have)
  • Hands-on experience building **multi-agent systems** using at least one platform: **Claude Code CLI**, **Cursor**, **Gemini CLI**, LangChain/LangGraph, AutoGen, CrewAI, or equivalent
  • Ability to design agent orchestration patterns: sequential chains, parallel fan-out, supervisor/worker hierarchies, reflection and self-critique loops
  • Experience with **tool use / function calling** in LLM agent contexts - designing custom tools agents invoke to read codebases, query databases, call APIs, or run test suites
  • Working knowledge of **agent memory strategies**: in-context state, vector store retrieval (RAG), structured external memory
  • Practical **prompt engineering** for code tasks: extraction, generation, validation, and iterative refinement prompts
  • Experience evaluating agent output quality; ability to design human-in-the-loop review workflows and validation harnesses
  • Familiarity with **LLM APIs**: Anthropic Claude, Google Gemini, OpenAI GPT-4o, or equivalent; understanding of context limits, token budgets, and cost management at scale
• Agentic AI Platforms -
  • **Claude Code CLI** | Agentic coding sessions, MCP tool integration, custom slash commands, BMAD/GSD agent workflows
  • **Cursor** | AI-native IDE, codebase-indexed agents, `.cursorrules` | enforcement, multi-file refactoring
  • **Gemini CLI** | Long-context legacy codebase analysis, Google | ecosystem integration, structured output pipelines
  • **LangChain / LangGraph** | Complex stateful agent chains, tool | registries, conditional routing, legacy extraction pipelines
  • **AutoGen / CrewAI** | Role-specialised multi-agent teams for parallel | workstreams (extractor + validator + generator)
• Modernisation & Legacy Expertise
  • 15+ years of software engineering; 3+ years in a tech lead, engineering solution designer role
  • Ability to read and reason about **COBOL, CICS transaction logic, IMS hierarchical data structures** - extracting business intent
  • Experience designing **strangler-fig**, **anti-corruption layer**, and **event interception** patterns in production migration contexts
  • Familiarity with **dual-write / dual-read** strategies and zero-downtime data migration pipeline design
• Modern Forward Engineering Stack (Must-Have)
  • Deep expertise in **Java 21** - virtual threads, records, sealed classes, pattern matching - and **Spring Boot 3.x**: REST, Security, Data, actuator, event-driven patterns
  • **MongoDB** - document schema design for migration from relational/hierarchical models, aggregation pipelines, indexing strategy
  • **Angular / TypeScript** - component architecture, RxJS, HTTP client, module boundaries replacing legacy UIs
  • **OpenAPI 3.1** contract-first API design; MapStruct, Lombok, JUnit 5, Mockito for production-grade code
  • **IBM MQ** integration and migration path experience toward modern messaging (Kafka, Azure Service Bus, or equivalent)
  • **Spring Expression Language (SpEL)** and rules-engine integration (Drools or equivalent) for migrating COBOL decision logic
• Cloud & Cloud-Native (Must-Have)
  • Hands-on production experience on **AWS, Azure, or Google Cloud Platform** - compute, managed databases, IAM, networking, serverless, and managed AI/ML services
  • **Docker** and **Kubernetes** - container design, deployment manifests, config maps, health probes, resource management
  • Cloud-native patterns: service mesh, circuit breakers, retry/backoff, distributed tracing (**OpenTelemetry**), structured logging
  • **Infrastructure-as-code**: Terraform and/or Helm - able to design, review, and govern agent-generated IaC
  • Twelve-factor application design applied to systems migrated from stateful legacy environments
• CI/CD & DevSecOps
  • Pipeline design and ownership in **GitHub Actions**, Jenkins, or Azure DevOps - including AI-assisted pipeline generation with engineer review gates
  • Automated quality gates: test coverage, SonarQube, OWASP dependency scanning, container image scanning, and **agent-output validation gates**
  • Environment promotion pipelines with migration-state awareness and feature-flag-controlled legacy cutovers
  • Audit trail design for agent-generated artefacts entering the delivery pipeline