Java Backend Engineer (AI)

________________________________________________________________________________ About the Role: We are looking for a senior, polyglot full-stack engineer to build and own services across a large-scale supply-chain analytics and alerting platform. You will work end-to-end — from streaming data ingestion and BigQuery analytics, through Java/Spring Boot APIs, to a modern React/Next.js frontend — and contribute to an emerging agentic-AI capability. This is a hands-on role for someone who can operate as both an individual contributor and a lightweight architect, owning services outright and driving them to production.

You will routinely move between four stacks (Java/Spring Boot, Apache Beam on GCP Dataflow, Python/FastAPI, and React/Next.js), so we value breadth and the ability to context-switch as much as depth in any one area. What You'll Do: Design, build, and own backend services in Java / Spring Boot, exposing REST and GraphQL APIs over BigQuery and MongoDB. Build and harden streaming and batch data pipelines using Apache Beam on GCP Dataflow, ingesting high-volume supply-chain event feeds (shipping notices, inventory adjustments, orders, transfers) into BigQuery.

Author and tune analytical BigQuery queries that detect operational conditions and generate prioritized alerts; manage daily vs. weekly pipeline scheduling. Implement alert-lifecycle and worklist features (ingest, route, snooze, resolve, reactivate, dismiss, archive) with reliable, idempotent processing at scale. Optimize performance over heavy data aggregations — parallel query execution, caching layers, materialized views, and thread-pool tuning.

Build agentic-AI workflows on Python / FastAPI using LangGraph / LangChain and Vertex AI / LLMs, including human-in-the-loop email automation (SMTP/IMAP) and knowledge-base-driven prompting. Develop frontend features on React 18 / Next.js / TypeScript with Redux Toolkit redux-saga, including data-rich dashboards and conversational/agentic UI. Implement cross-cutting concerns: role-based access, secure impersonation, scoped JWTs, audit logging, and PowerBI embedding/report filtering.

Own quality end-to-end: test coverage, security/vulnerability remediation, framework upgrades, and production incident resolution. Collaborate across teams, contribute to HLD/LLD design discussions, and review peers' pull requests. Must-Have Qualifications: 8 years of professional software engineering experience, with strong depth in Java and Spring Boot (REST and GraphQL).

Proven experience building data pipelines with Apache Beam / GCP Dataflow (or equivalent: Spark, Flink) and querying large datasets in BigQuery (or Snowflake/Redshift). Solid Python experience, ideally with FastAPI and modern async patterns. Production experience with React / Next.js / TypeScript and state management (Redux Toolkit, redux-saga, or similar).

Strong with both SQL and NoSQL data stores (PostgreSQL/BigQuery and MongoDB). Hands-on GCP experience (Dataflow, BigQuery, Pub/Sub, Vertex AI); comfort with containerization (Docker/Kubernetes) and CI/CD. Demonstrated ability to own a service end-to-end and operate independently with minimal supervision.

Strong fundamentals in performance tuning, concurrency, testing (TDD/integration tests), and debugging production incidents. Nice-to-Have: Experience building agentic-AI / LLM workflows (LangGraph, LangChain, function/tool calling, RAG, human-in-the-loop). Supply-chain, retail, inventory, or logistics domain knowledge.

Experience with Kafka, event-driven architectures, and high-reliability ingestion (failover, locking, exactly-once semantics). Multi-cloud exposure (AWS and/or Azure in addition to GCP). Experience with PowerBI or other embedded analytics/BI tooling.

Familiarity with AI-augmented development tooling (e.g., Claude Code) to accelerate delivery. What Kind of Engineer Succeeds Here: This role suits a senior generalist / polyglot full-stack engineer — someone who is genuinely comfortable across backend, data engineering, AI, and frontend, rather than a specialist in only one layer. The ideal candidate: Thinks in systems and data flows, not just individual endpoints.

Is pragmatic and delivery-focused, balancing clean architecture with shipping reliable features. Can pick up an unfamiliar stack quickly and become productive within it. Operates with ownership and low ego, equally happy fixing a production data incident, designing a new service, or pairing with frontend.

Brings architectural judgment (HLD/LLD, trade-off analysis) while remaining deeply hands-on.