Infrrd.ai - Senior Java Developer

About The Role You will own backend services that process large, complex mortgage documents at scale, designing the pipelines, integrating LLMs, and keeping the whole thing fast, accurate, and cost-effective in production. This is a hands-on senior role with real ownership: you will make architecture calls, drive them to a decision, and set the quality bar for engineers working alongside you.

Responsibilities

• Build high-throughput document pipelines: Asynchronous, parallel processing with batching, retries, backpressure, and idempotent reprocessing for large documents and large data volumes.
• Work with the latest LLMs: Build against current LLM API timeouts, token and cost controls, response validation, and automatic failover between providers.
• Engineer for resilience: Circuit breakers, fallback chains, and graceful degradation so a single upstream failure never takes the pipeline down.
• Debug production: Investigate extraction failures, latency spikes, and cost regressions using dashboards and metrics, and fix the root cause.
• Raise the bar on the team: Review PRs, break features into clear tasks, unblock junior engineers, and write design docs that hold up to scrutiny across time zones.

Requirements

• Bachelor's degree in Computer Science, Information Technology, or a related field.
• 4+ years of hands-on Java development, including at least 2 years owning a production service.
• Excellent command of Core Java, with real depth in multithreading and concurrent architecture design.
• Strong with Spring Boot and Spring Data; comfortable in microservices and REST architecture.
• Strong working knowledge of NoSQL DB (preferred: MongoDB); familiar with relational databases (MySQL or PostgreSQL).
• Comfortable with Docker and Kubernetes, and at least one cloud platform (AWS, GCP, or Azure).
• Strong debugging and problem-solving skills with a solid command of data structures.
• Reasons about trade-offs in cost, latency, and accuracy, not just correctness.
• Has informally mentored or guided junior engineers.
• Operates independently with minimal supervision, and proactively flags risks.
• Excellent written communication; much of the collaboration is asynchronous and across time zones.

Good To Have

• Exposure to LLM or GenAI application development, prompt iteration, evaluation, or accuracy measurement.
• Working knowledge of Python.
• Experience with streaming queues like Kafka or RabbitMQ.
• Background in Document AI, OCR, or the mortgage/fintech domain.
• Familiarity with observability tooling (Grafana, Metabase, ELK).

(ref:hirist.tech)