Vetted Stakeholder Communication Professionals

“Engineer using AI deeply in real production workflows, not just for code generation: they built agents for PR reviews and incident debugging that reportedly reduced review time by 50% and sped root-cause analysis by 30%. They also designed a three-agent personalization pipeline for real-time navigation curation, showing hands-on experience with multi-agent systems, orchestration, and rule-based refinement.”

Pavan Devulapalle

Screened ReferencesModerate rec.

Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development

Seattle, WA3y exp

“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”

“Built and shipped a map-embedding SDK (published to npm) for Walmart apps, solving key performance issues with real-time streaming (WebSockets) and Canvas rendering while prioritizing developer experience. Also applies LLM/agentic patterns in production workflows—using diagnostic agents and human-in-the-loop escalation to detect and resolve issues (e.g., voice agent loops caused by RAG API failures). Has sales-engineering experience supporting enterprise renewals, including a million-dollar contract renewal while at Siemens working with Ford stakeholders.”

“UX/product designer with deep healthcare and mission-critical software experience across 3M Healthcare, GE Healthcare, and Motorola. Has designed complex lab automation and radiology workflows end-to-end, combining rigorous field research with hands-on engineering (HTML/CSS/TypeScript/React, Storybook) and even building live prototypes (Firebase + Angular) and production-ready UI control libraries to ship.”

Benjamin Kozel

Screened

Mid-level Machine Learning & Software Engineer specializing in RAG systems and ML infrastructure

Atlanta, GA4y exp

“Built and deployed an in-house RAG LLM system ("MONTY") using LLaMA 3B + FAISS to help teams quickly understand long internal/external specifications. Delivered usable production performance despite severe compute limits (single RTX 3080) by tuning retrieval/reranking and model choice, and is planning a LightRAG/knowledge-graph rewrite to improve accuracy and latency.”

“Fleet and enterprise seller with experience at Tesla and Lucid, specializing in creating demand for EV solutions in low-inbound markets. Stands out for closing consultative, multi-stakeholder fleet deals by combining pilot programs, technical education, and outcome-driven messaging— including a 20-unit, ~$3M police department deal that helped them exceed quota.”

“Built ZenDraft and brings an operator's lens to founder sourcing and evaluation. Demonstrated ability to run structured outbound, convert cold outreach into product-shaping conversations and pilot introductions, and manage an early-stage investment-style pipeline with clear decision frameworks.”

“Analytics professional with experience at McKinsey & Company and Dell Technologies, focused on turning messy operational and business data into trusted dashboards and decision tools. They combine SQL, Power BI, and Python to solve data quality issues, define metrics like retention, and deliver measurable impact such as a roughly 30% reduction in manual reporting time.”

“Built and deployed a production AI/ML SRE copilot that uses RAG over real-time Splunk signals plus deployment/runbook data to generate grounded incident summaries and next steps, cutting time-to-contact by 30%. Treats the knowledge corpus like a production dataset (quality gates, semantic chunking, metadata enrichment) and runs golden-dataset automated evals to ensure reliability, while partnering closely with ops/support leaders through discovery sessions and metric-driven demos.”