Vetted Systems Engineers in California

“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.”

Pankaj Goyal

Screened

Director-level Engineering Leader specializing in FinTech, IAM, and AI/ML platforms

SF Bay Area, CA22y exp

“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.”

Param Yanamandra

Screened

Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation

Campbell, CA21y exp

“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”

Derek Tuggle

Screened

Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls

San Francisco, CA6y exp

“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”

“AppSec/customer security specialist with Atlassian enterprise cloud migration experience, advising on SSO and API token hardening and driving adoption through phased rollouts. Implemented Snyk (SCA) and SonarQube (SAST) in Bitbucket Pipelines with Jira-based vuln workflows, cutting critical vuln MTTR from 30 to 7 days for financial services customers. Strong in SSO troubleshooting (Okta/SAML) and secure AWS/EKS-based agent integrations with secrets management and Datadog observability.”

Aidan Anderson

Screened

Director-level engineering leader specializing in AI, security, and FinTech SaaS

California, USA12y exp

“Engineer and bootstrap founder who has worked across roughly five venture-backed startups from pre-seed through Series E. He combines hands-on startup execution with strong judgment around product-market fit, founder fit, and customer validation, while independently building profitable, self-owned micro SaaS ventures on the side.”

Karan Variyambat

Screened

Mid-level Machine Learning Engineer/Researcher specializing in computer vision and multimodal AI

San Diego, CA3y exp

“Developed a production wildfire smoke detection system where smoke is visually subtle and easily confused with fog/clouds; addressed this with a hybrid CNN+LSTM+ViT model and multimodal weather features to reduce false positives. Experienced running scalable, reproducible ML pipelines on shared GPU infrastructure using Slurm and Kubernetes-style batch jobs with checkpointing, retries, and rigorous error analysis.”

“ML/NLP practitioner who built customer-feedback topic modeling (NMF + TF-IDF) to diagnose chatbot-to-agent handovers and drove product/ops changes that reduced operational costs by 20%. Also developed LSTM-based intent recognition using Word2Vec/GloVe embeddings for semantic linking, and deployed an LSTM autoencoder for fraud anomaly detection that cut false positives by 25% while capturing 15% more fraud in A/B testing.”

Somil Shah

Screened

Mid-level AI/ML Engineer specializing in generative AI, RAG platforms, and LLM agents

San Francisco, CA4y exp

“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”