Vetted Terraform Professionals

“Senior engineering/technology leader who has defined and delivered a multi-year roadmap to modernize platforms and embed AI, leading global teams through cloud-native and microservices migrations on AWS/Azure. Demonstrated measurable outcomes including 99.99% uptime, 40% fewer incidents, 25% faster delivery, 5x scalability, and $30M in new business opportunities, while scaling a 100+ person distributed org with strong OKR-driven execution and mentorship culture.”

Nicholas Ogura

Screened ReferencesStrong rec.

Executive Platform & Security Engineering leader specializing in multi-cloud Kubernetes and FinTech

San Diego County, USA18y exp

“Startup-focused infrastructure/security leader who stepped into head of engineering and re-platformed an entire product end-to-end in 3 months to meet launch. In crypto/fintech, recognized the market-data system as an ETL/data product and rebuilt it as a separable, securely accessible platform—prompting inbound interest within a week—while advocating an open-source-first observability stack (Prometheus/Grafana/Loki) to avoid vendor lock-in.”

michael avrahamov

Screened

Executive engineering leader specializing in AI-first SaaS and cloud platforms

San Francisco Bay Area, CA22y exp

“Senior engineering executive who operated as an org-level CTO at both ServiceNow and Oracle, leading global teams up to 150 engineers and driving cloud-native, multi-tenant platform architecture. Particularly strong in observability, streaming systems, and security, with experience delivering large-scale systems ingesting data from millions of agents while building high-performing, high-DORA engineering organizations.”

“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”

Brian Gomez

Screened

Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision

United States12y exp

“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”

“Frontend engineer focused on sophisticated, high-performance browser UIs for internal operations teams. They have owned real-time monitoring and transaction-tracking interfaces, combining deep browser performance knowledge with user-centered workflow design to deliver sub-second responsiveness, 60 FPS rendering, and a 30% improvement in task completion speed.”

Gowtham Sai Reddy Maddireddy

Screened

Mid Software Engineer specializing in AI platforms and backend systems

USA3y exp

“Built and shipped AI-powered enterprise search and workflow automation features end to end across React/TypeScript, FastAPI, PostgreSQL, and OpenAI/LangChain. Demonstrates practical production experience with RAG, FAISS, monitoring, and human-in-the-loop safeguards, with reported gains of 35% faster response times and 50% higher automation efficiency.”

“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”

Sahitya Thota

Screened

Staff/Tech Lead Software Engineer specializing in identity, data platforms, and cloud systems

Fremont, CA20y exp

“Engineering leader/player-coach who built and owned end-to-end sales-facing data products, including a 360° advertiser insights platform and a sales AI agent for natural-language access to insights. Demonstrated strong architecture and reliability chops (event-driven redesign to eliminate Hive-query throttling; batching/caching to reduce fan-out) plus incident ownership around ads attribution consistency. Also has 0→1 experience building graph-based recommendation/matching systems with explainability and tight user feedback loops.”

“Founder building an enterprise agentic AI startup who has already raised about $100K (friends & family plus self-funding). Has prior and current experience engaging VCs (including outreach to both large and small firms) and emphasizes demo-driven pitching, enterprise customer validation, and targeting billion-dollar market opportunities.”

David Lapsley

Screened

Executive Engineering Leader specializing in AI infrastructure and cloud platforms

Tampa, FL14y exp

“Founder building Shipkode.ai, an enterprise SaaS product using generative AI to automate the front half of the software development lifecycle—from idea and customer feedback to engineering-ready specifications—with a beta planned for the first week of April. Has spoken with 24+ VCs over two years and mentors cybersecurity startups through the Tampa Bay Wave accelerator, emphasizing rigorous customer discovery to avoid misaligned product development.”

“Backend/full-stack engineer with experience building large-scale systems on Salesforce and eBay, including a sharded Oracle-backed personalization store used by millions of sellers and a Spring Boot microservice that executes scheduled payouts at high volume. Emphasizes production readiness via state-machine-based idempotency, strong observability (Splunk/Elasticsearch/PagerDuty), and extensive Selenium/Sauce Labs automation.”

“Backend/AI engineer currently at Stripe building Minions, an internal LLM-based developer agent that automates code generation, bug fixes, and PR creation. They combine strong production LLM architecture skills with reliability engineering, having improved routine PR merge times by ~40-45%, lowered post-merge bug rates by 12%, and previously built an invoice-processing pipeline that achieved 91% straight-through processing in a B2B payments context.”