AI Engineer IT & Data Development

01.tech; Australia; Remote; AUD 140,000 - 210,000; Full time

Benefits offered by this job

Medical insurance

Benefits allowance

Training reimbursement

Digital library

Psychology sessions

Relocation assistance

Remote or office work

Autonomy & ownership

Job summary

01.tech is seeking a seasoned backend developer to design and build a Java-based LLM service with a unified API powering the platform's player chat and help center. You will own core infra and ship production features.

The role covers knowledge base ingestion, RAG pipelines, guardrails for safety and compliance (including responsible gambling), multilingual support, and seamless handoff to human agents. You will optimize costs and enable observability while collaborating across teams in a

Qualifications

• 4+ years backend development with Java (17+), Spring Boot, REST/gRPC, Kafka or RabbitMQ, PostgreSQL, Redis, Docker and Kubernetes.
• Experience shipping at least one LLM-powered production application with real users.
• Hands-on with RAG systems: chunking, embeddings, hybrid retrieval and reranking.
• Experience with vector databases and search engines (pgvector, Qdrant, OpenSearch/Elasticsearch kNN).
• Experience integrating LLM provider APIs (OpenAI, Azure OpenAI, etc.) with function/tool calling and structured outputs.
• Ability to evaluate LLM systems with quantitative metrics and automated evaluation frameworks.

Responsibilities

• Design and build a Java-based LLM service with a unified API for multiple products (player chat, help center).
• Develop knowledge base ingestion pipelines: parsing sources, chunking, embeddings, reindexing and data versioning.
• Build and optimize RAG pipelines and semantic search with hybrid retrieval and multilingual support.
• Implement LLM guardrails for prompt safety, PII protection, grounded responses, and iGaming compliance.
• Enable handoffs to human agents and integrate AI with internal services via tools and structured outputs.
• Create automated LLM evaluation pipelines using golden datasets and regression testing.
• Optimize performance and costs via semantic caching, model selection, latency tuning and token monitoring.
• Establish production observability with tracing, dashboards, and CSAT metrics.

Skills

Java

Spring Boot

REST/gRPC

Kafka/RabbitMQ

PostgreSQL

Redis

Docker

Kubernetes

LLM pipelines

RAG systems

Tools

pgvector

Qdrant

OpenSearch/elasticsearch kNN

LangChain4j

LangChain

LlamaIndex

OpenTelemetry

Job description

• Design and build a Java-based LLM service with a unified API serving multiple products, including the platform's player chat and help center
• Develop knowledge base ingestion pipelines, covering source parsing, chunking, embedding generation, incremental reindexing, and data versioning
• Build and optimize RAG pipelines and semantic search, including hybrid retrieval (vector search + BM25), reranking, source-grounded responses with citations, and multilingual query support
• Implement LLM guardrails to prevent prompt injection, protect personally identifiable information (PII), keep responses grounded in approved knowledge sources, and enforce iGaming-specific restrictions related to responsible gambling, identity verification, and payments
• Enable seamless handoff to human support agents and integrate the AI assistant with internal services through tool calling and structured outputs
• Build automated LLM evaluation pipelines using golden datasets, LLM-as-a-judge approaches, faithfulness and answer relevance metrics, and regression testing whenever prompts, models, or search indexes change
• Optimize LLM performance and operating costs through semantic caching, model selection based on query type, latency optimization, and token usage monitoring
• Implement production observability, including LLM tracing, quality dashboards, and business metrics such as automated resolution rate, escalation rate, and customer satisfaction (CSAT)

Requirements

• 4+ years of backend development experience with Java, including Java 17+, Spring Boot, REST/gRPC, Kafka or RabbitMQ, PostgreSQL, Redis, Docker, and Kubernetes
• Experience shipping at least one LLM-powered application to production with real users, and the ability to discuss its architecture, traffic, and quality metrics
• Hands-on experience building RAG systems, including chunking strategies, embedding model selection, hybrid retrieval, and reranking
• Ability to diagnose LLM response quality issues and distinguish between retrieval failures, prompt-related problems, and model limitations
• Experience with vector databases and search engines such as pgvector, Qdrant, OpenSearch/Elasticsearch kNN, or equivalent technologies
• Experience integrating LLM provider APIs (OpenAI, Anthropic, Azure OpenAI, etc.), including function/tool calling, structured outputs, streaming, retries, model fallbacks, and rate limiting
• Experience evaluating LLM systems using quantitative metrics and automated evaluation frameworks such as Ragas, promptfoo, DeepEval, or custom evaluation datasets
• Strong software engineering practices, including automated testing, CI/CD, code reviews, logging, monitoring, and ownership of production services
• Nice to have experience with Spring AI or LangChain4j, along with familiarity with Python-based LLM frameworks such as LangChain or LlamaIndex and the ability to adapt their approaches to Java; experience with LLM observability tools such as Langfuse, Arize Phoenix, or OpenTelemetry; hands-on experience with semantic caching and LLM inference cost optimization; experience building multilingual AI assistants and knowledge base search systems; experience in iGaming, betting, FinTech, or customer support automation, including an understanding of industry-specific compliance requirements
• The opportunity to work on large-scale products and contribute to their development
• A flexible work environment built on autonomy, ownership, and trust, without micromanagement
• The option to work fully remotely or from a modern office
• Medical insurance coverage in countries where the company operates
• An annual $2,000 benefits allowance after the probation period, which can be used for health insurance upgrades, language learning, and sports activities
• Partial reimbursement for professional training, courses, and participation in industry conferences
• Access to a corporate digital library
• Company-funded weekly online sessions with a psychologist
• Relocation opportunities to countries where the company has offices, with relocation assistance available after the probation period, subject to approval