From founding engineers to VP-level leaders, individual contributors to GTM teams — if it touches AI, we can source it.
Design, build, train, and deploy machine learning models. From fine-tuning Llama and Mistral to building proprietary models from scratch.
Build RAG pipelines, fine-tune foundation models on proprietary data, integrate LLMs into production SaaS products and enterprise workflows.
Keep models running in production. GPU cluster management, inference cost optimization, model monitoring, A/B testing, and deployment pipelines.
Optimize AI interactions for business ROI, architect system prompts, prevent jailbreaks, and design automated agentic workflows at scale.
Improve context understanding, handle multilingual inputs, reduce hallucinations, and power enterprise chatbots and document analysis systems.
Push the boundaries of what AI can do. New model architectures, improved reasoning systems, agentic workflows. Top labs recruit these non-stop.
Chief Technology and Information Officers who can build and scale AI-native engineering organizations from the ground up.
Engineering leaders who can hire, manage, and retain top AI talent while shipping production systems at speed and scale.
Systems thinkers who design scalable, fault-tolerant AI infrastructure — from data pipelines to model serving to API layers.
Leaders who bridge AI technical capability and business outcomes — driving digital transformation and AI-first product strategy.
Front-line people leaders who keep high-performing AI teams productive, engaged, and growing while maintaining technical credibility.
Build and manage cloud infrastructure for AI workloads — GPU clusters, distributed compute, autoscaling, and cost optimization.
CI/CD pipelines, orchestration, containerization, and configuration management keeping AI systems reliable at enterprise scale.
Design enterprise-grade cloud architectures optimized for AI/ML workloads, multi-region deployments, and data sovereignty requirements.
High-throughput networking for AI inference and training — low latency, high bandwidth, and fault tolerance for distributed model training.
Manage Linux-based AI compute environments, virtualization, storage, and the operational backbone of AI research and production systems.
Build the pipelines that feed AI models. ETL, data warehousing, streaming, and the data infrastructure that makes ML possible at scale.
Transform raw data into business intelligence, model features, and predictive insights that feed both strategic decisions and AI systems.
The unsexy but critical role — every model needs clean, labeled training data. Data annotation leads own quality, consistency, and throughput.
Build dashboards and reporting systems that make AI model performance, business metrics, and data quality visible to stakeholders.
Secure AI model endpoints, cloud infrastructure, and data pipelines. Defend against model extraction, prompt injection, and data poisoning.
Integrate security into the AI development lifecycle. Threat modeling, SAST/DAST, secure code review, and vulnerability management for AI apps.
Build security pipelines that move at the speed of AI development — shifting left without slowing down model deployment and iteration cycles.