Description
365scores is seeking a self-driven AI Engineer to join our Core AI team. You will own the full product lifecycle of end-to-end generative AI systems—from data extraction and backend database design to multi-agent orchestration, evaluation, and live production monitoring.
Your domain will bridge two high-stakes engineering environments: high-throughput batch processing for deep pre-match analysis and ultra-low-latency generation (sub-second) for live match streaming. You will operate with high autonomy to drive architectural decisions and shape our applied AI infrastructure.
Responsibilities
Multi-Agent Orchestration & Systems: Design, build, and deploy specialized LLM agents for data triage, real-time content generation, multilingual translation, and automated self-critique using agentic frameworks (e.g., Google ADK).
Harness & Memory Engineering: Build agent harness frameworks, manage stateful session handling, and implement scalable short-term and long-term memory architectures.
Real-Time & Batch Workflows: Architect dual-track AI workflows: optimize pre-match pipelines for analytical depth, and tune live match generation pipelines for sub-second response times.
Model Routing & Cost Optimization: Implement intelligent dynamic routing between commercial and open-source models (via OpenRouter, AWS, GCP, and Databricks) to balance latency, cost, and quality.
Data Engineering & RAG: Design robust database schemas and data pipelines that fuel production RAG architectures and dynamic agent context.
Evaluation & Observability: Build automated critique loops (LLM-as-a-judge) to measure output precision, while implementing full observability/logging to trace agent reasoning steps in production.
Requirements
Engineering & Backend Foundations: Strong background in software engineering, database architecture, and building scalable production backends.
Applied AI & Agentic Systems: Proven experience building production-grade GenAI applications, multi-agent systems, and working with agentic frameworks.
AI-Assisted Software Engineering: Practical mastery of AI-assisted development tools—choosing the optimal model per coding task, providing structured context, and avoiding unverified "vibe coding."
Cloud & Infrastructure: Hands-on experience with modern cloud ecosystems (AWS, Databricks, or GCP) and managed LLM endpoint deployment.
Prompt & Evaluation Engineering: Deep understanding of prompt optimization, prompt caching techniques, and automated evaluation (Evals) pipelines.
Technical Autonomy: Track record as an autodidact who rapidly researches, benchmarks, and adopts new frameworks in a fast-moving AI ecosystem.
Nice to Have-
Direct experience building real-time event streaming systems for sports or live media.
Familiarity with specialized decision models (e.g., TypeSafe AI / Jev) or custom router architectures.
Active contributions to open-source agentic tools, frameworks, or evaluation benchmarks.
Sport fanatic - Advantage
Summary
Build and deploy end-to-end generative AI systems, including multi-agent orchestration, RAG pipelines, model routing, evaluation, and production monitoring across batch and low-latency live workflows. Requires strong software engineering, backend and database architecture, cloud experience, and production GenAI or agentic systems expertise.
Responsibilities
Design, build, deploy LLM agents; build agent harness frameworks and memory architectures; architect dual-track AI workflows; implement model routing and cost optimization; design database schemas and RAG pipelines; build evaluation and observability.
Qualifications
Proven experience building production-grade GenAI applications, multi-agent systems, software engineering, database architecture, scalable backends, cloud ecosystems, prompt optimization, caching, and evaluation pipelines; autodidact.
Experience requirements
Strong background in software engineering, database architecture, scalable production backends, applied AI/agentic systems, cloud infrastructure, prompt and evaluation engineering, technical autonomy.