• 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.

• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams. • Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value. • Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces. • Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions. • Apply strong systems thinking to ensure reliability, performance, security, permissions, and auditability across platforms. • Collaborate closely with engineers, teams, and stakeholders across the company to identify high-impact opportunities and drive adoption. • Participate in production support and incident response for owned systems, helping define and uphold reliability and operational best practices. • Mentor AI team members through code reviews, design feedback, and hands-on guidance, raising the overall quality bar of the team.

• Lead the design and development of scalable machine learning infrastructure on AWS • Work closely with product teams to develop MVPs for AI-driven features • Create and enhance monitoring and alerting systems for machine learning models • Enable various departments to leverage AI/ML models for different use cases. • Offer expertise in debugging and resolving issues related to ML models in production • Design and scale ML architecture to support rapid user growth • Conduct code reviews, mentor team members and elevate overall team capabilities through knowledge sharing

• Design and build production-grade AI systems • Make principled architecture choices regarding RAG and fine-tuning • Build comprehensive evaluation and observability frameworks • Create production-quality Python services • Manage the economics of LLM usage • Partner with Product and Data Science teams • Mentor junior engineers on AI craft • Establish standards for performance and data governance