Pre-screened and vetted.
Executive Technology Leader (CTO/EVP) specializing in product-led SaaS and data-driven platforms
Senior Data Scientist / ML Engineer specializing in LLMs, generative AI, and MLOps
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLM apps and MLOps
Senior Full-Stack Engineer specializing in cloud-native and AI-powered enterprise products
Senior Full-Stack Python Engineer specializing in cloud microservices and AI/LLM systems
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level Python Backend Developer specializing in cloud-native microservices and AI/ML platforms
Junior AI/ML Software Engineer specializing in NLP, LLM evaluation, and recommendation systems
Senior Software Engineer specializing in payments, billing, and fraud/risk platforms
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Senior Data Engineer specializing in cloud-native data platforms and streaming pipelines
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Senior Full-Stack & AI/ML Engineer specializing in cloud-native SaaS and IoT analytics
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
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.”
Director-level Customer Success & GTM leader specializing in Cloud, AI, and Enterprise SaaS
“Commercial/GTN leader with GCP experience managing multi-year, multi-megawatt AI/GPU infrastructure commitments, owning segment P&L and governance for take-or-pay/reserved capacity. Drove a major client partnership scaling ARR from $50M to $100M in 18 months by aligning Product/Engineering, GTM, and infra teams and building flexible, margin-protective commercial structures. Known for speeding hyperscaler procurement/security reviews (FedRAMP/SOC2, IAM, data residency) and operationalizing multi-region delivery with landing zones and IaC.”
Director-level Data Architecture & Governance leader specializing in cloud analytics platforms
“Technology/architecture leader with Accenture experience delivering data- and AI/ML-driven products, including a legal contract search solution and customer sales analytics for AWS. Known for scaling distributed teams (onshore/offshore), making pragmatic architecture decisions, and solving hard data problems (proprietary sources, data quality) while implementing scalable integrations like Redshift-to-Salesforce via parallelized pipelines.”
Senior Data Engineer specializing in cloud data platforms and analytics pipelines
“Data engineer focused on building and operating reliable Airflow-orchestrated pipelines into BigQuery, including daily billing ingestion (~1GB/day) and ad platform (Facebook/LinkedIn) data collection. Implemented end-to-end data quality checks plus org-wide incident response automation integrating PagerDuty, Slack, and Jira, and has experience executing large backfills (4–5TB) via time-window batching.”
Mid-level Software Engineer specializing in distributed systems on AWS
“Data/infra engineer with AWS DynamoDB experience who has shipped reliability-critical systems (Global Tables replica repair protocol) and customer-facing service rollouts using canary/percentage-based deployments, strong observability, and rollback strategies. Also built end-to-end Airflow pipelines producing weekly automated reports over ~10TB of advertising segment data, with rigorous week-over-week data quality validation.”
Director-level Engineering Leader specializing in FinTech, IAM, and AI/ML platforms
“Player-coach backend leader at PostLo who led a major backend architecture upgrade to enable AI-driven features by separating transactional systems from AI workloads (vector embeddings/image validation) and adding async processing for heavy jobs. Also owned production reliability improvements (query/index optimization, workload isolation, monitoring and load testing) and translated an ambiguous retention goal into a shipped cashback rewards feature with auditable transactions.”
Executive Engineering Leader specializing in Blockchain, FinTech, and SaaS platforms