AI Engineer

🕒 February 25

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Fastino Labs

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

🔥 Funding within the last year

💰 Seed on 2025-08

Artificial Intelligence • SaaS • Enterprise

Fastino Labs is a company that builds production-ready, task-specific small language models (SLMs) and a fine-tuning platform focused on fast, efficient extraction and classification for agentic AI. Their GLiNER models deliver low-latency, small-footprint inference (often <50 ms and ~200M parameters) that can run on CPU and edge hardware, and can be deployed privately (VPC, on-premises, or on-device). Fastino provides tools to generate synthetic datasets, fine-tune models for domain-specific tasks (e. g. , PII detection, clinical data extraction, ad detection, knowledge-graph extraction), and ship optimized model weights for production use, targeting developer and enterprise workflows.

📋 Description

• Innovate at the edge of efficiency by designing and deploying high-performance agentic systems that leverage Fastino’s optimized model architectures to outperform traditional LLM benchmarks. • Bridge the gap between research and production by collaborating with engineering teams to turn novel architectural breakthroughs into scalable, low-latency solutions for enterprise customers. • Drive rapid, iterative prototyping of AI functionalities, refining model performance and task-accuracy based on real-world telemetry to ensure specialized models meet rigorous developer standards. • Own the stability and throughput of inference pipelines, proactively solving scalability bottlenecks to ensure models deliver consistent, reliable performance under massive operational loads. • Architect large-scale data and fine-tuning strategies to continuously improve the precision and domain-specific reliability of the Fastino models.

🎯 Requirements

• Required:2+ years of hands-on experience in AI/ML engineering roles • Required: Demonstrated proficiency with LLMs and a track record of applying AI/ML techniques to solve complex, unstructured problems • Required: You are comfortable working across the stack from prompt engineering and vector DB tuning to Kubernetes deployment and API design. • Optional: Experience building microservices that handle high-concurrency agentic workloads. • Optional: Familiarity with GLiNER or other information extraction architectures.