Pre-screened and vetted.
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Executive FinTech Founder and Software/Finance Leader specializing in data pipelines and valuation
Executive Technology & Resilience Leader specializing in AI, multi-cloud, SRE, and cyber resilience
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Principal business value leader specializing in AI, data, and cloud transformation
Executive CTO and serial founder specializing in AI platforms and Financial Services
Director-level Software Development leader specializing in AI/ML platforms and cloud architecture
Senior Software Engineer specializing in cloud security and identity management
Senior Full-Stack & AI/ML Engineer specializing in cloud-native SaaS and IoT analytics
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Principal/Staff Engineer specializing in platform architecture, AI/ML, and distributed systems
Mid-level Software Engineer specializing in backend and distributed systems
Senior Software Engineer specializing in AI infrastructure and distributed systems
Senior AI Product Manager specializing in GenAI platforms and agentic commerce
Senior Software Engineer specializing in AI for Healthcare and Enterprise SaaS
Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning
“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”
Principal Strategic Partnerships Executive specializing in cloud, AI, and global GTM
“CRO/operator with experience at both AWS and a rapidly scaling Hawaii-based startup (Mana'olana International), focused on building lightweight but scalable operating systems. Known for implementing QBR/OKR rhythms, capacity/territory planning, KPI dashboards, and ROI-tied funding controls, and for mentoring founders through GTM accelerator work (Blue Startups) using a minimum-viable-process approach.”
Director-level Vaccine R&D leader specializing in clinical immunology and translational biomarkers
“Operations and clinical-development advisor currently supporting two early-stage biotech companies (vaccines and kidney transplant therapeutics). Brings 20 years of vaccine/clinical development experience from Sanofi and Pfizer, and has directly guided a CEO by connecting them with key clinical scientists and regulatory experts to support IND strategy and clinical advancement decisions.”
Junior Software Engineer specializing in distributed systems and machine learning
“Google backend engineer with strong experience in large-scale identity, membership, and access-control systems. Notable work includes reconciling customer IDs across 2B+ roster records and leading a 0-to-1 Drive sharing feature to classify external users as crossover members, with a strong emphasis on correctness, rollout safety, and low-latency service design.”
Mid-Level Software Engineer specializing in cloud infrastructure and data systems
“Backend engineer who helped redesign and refactor Forma’s backend during an app rewrite, emphasizing modularity, maintainability, and A/B testing support while delivering feature parity on a quarter-long timeline. Led a careful database migration using parallel databases with schema differences, validating integrity via staging and SQL checks, and has experience debugging subtle computer-vision overflow edge cases.”
Mid-level Data Engineer specializing in AI/ML platforms and cloud data pipelines
“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”