Vetted Deep Learning Professionals

“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”

Venu Dave

Screened ReferencesStrong rec.

Junior Software Development Engineer specializing in backend data platforms and LLM applications

New York, NY3y exp

“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”

“Seasoned global CTO and executive leader with 20+ years of experience, including a $389M non-founder exit to a Fortune 10 acquirer. Now building a pre-seed AI-driven diagnostic imaging platform for pets with web and mobile products, beta customers, and a patent-pending solution aimed at saving pet lives.”

“Engineering leader (12+ years Director, 17 years lead) focused on developer productivity and platform/framework work across Oracle, PlayStation, Workday, and CafePress. Notable for building distributed teams from scratch and delivering high-impact platform architecture—e.g., re-architected PlayStation’s upload pipeline to support 500GB–5TB submissions using browser-to-AWS chunked uploads with SNS/SQS and deduplication/resume support.”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

“VC-focused sourcing candidate with a multi-channel approach spanning technical research communities (GitHub/arXiv/Hugging Face) and LinkedIn, plus access to founder networks like NVIDIA Inception. Has experience initiating cold founder relationships and progressing them through intro calls into deeper technical and business diligence, with a structured weekly pipeline/partner update cadence and thematic notes on frontier technologies.”

Aidong Chen

Screened

Executive Data Platform Engineer specializing in kdb+/q and time-series systems

Orion Township, MI12y exp

“Veteran kdb+/q engineer with 17 years of experience applying time-series architecture across finance, autonomous driving, semiconductors, and scientific data systems. He describes building high-scale CEP and sensor-data parsing systems, achieving a 10x ingestion speedup through storage redesign, and even contributing the original remote_execute concept to PyKX.”

“Customer-facing technical professional with Amazon incident-management experience who helps drive adoption of complex ML/LLM solutions by delivering hands-on demos and rapid model fine-tuning. Applies a disciplined debugging approach (repro + logs/metrics + severity triage) and maintains runbooks to resolve SEV2 issues in ~1 hour, while also partnering with sales/customer teams to ship patches and new features based on feedback.”