Vetted Prompt Engineering Professionals

“Deloitte consultant who led cross-functional teams delivering a Snowflake/AWS data ingestion, warehousing, and analytics platform, with a strong track record of executive alignment and risk mitigation. Built reusable business-development accelerators (including an end-to-end Appian app and a Java integration-config tool) credited with helping secure $75M+ in contracts, and has high-confidentiality experience consulting for DoD and FDA.”

“CIO-level technology leader (most recently at Sovos) who owned the full tech roadmap across product, infrastructure, and corporate IT, scaling engineering across 14 countries with an architectural review board and standardized security/observability. Hands-on in high-severity incidents (ransomware) while managing executive/client communications, and drove a reported 40% product-velocity lift by adopting AI code assistants and agentic AI (Devin) alongside Kubernetes + Bottlerocket for secure scalability.”

“Built and deployed a production chatbot on osvaldocalles.com and iterated through real-world LLM engineering issues: model quota/cost tradeoffs (migrating to Nova Pro), RAG accuracy via semantic chunking, AWS IAM/guardrail/security pitfalls, and Lambda/API Gateway streaming constraints (prefers JS for streaming layer). Experienced with agent orchestration using Strands SDK (AWS-focused) and LangGraph (Vercel/container deployments), plus evaluation pipelines using LLM-as-evaluator, dashboards, and staged model rollouts.”

“Product and consumer growth professional from a B2C app background, focused on improving retention/activation and driving revenue through customer/product data. Experienced in maintaining BI layers and dashboards and running KPI-driven analysis across returns/refunds operations (NPS/CSAT, TAT, repeat complaints). Familiar with core F2P monetization mechanics and how to evaluate IAP offers via A/B testing; has shipped/managed products on mobile and web.”

“Director of Engineering at Proofpoint for 8 years, leading architecture and integration of Java microservices within a detection platform. Demonstrates pragmatic delivery leadership—incurring short-term cost to meet launch deadlines, then systematically paying down technical debt and optimizing AWS spend—plus a disciplined, long-horizon approach to backward-compatible API/schema evolution across many dependent services.”

“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.”

“Built and deployed a production LLM-powered clinical support assistant at BJC HealthCare (RAG + transformer) to answer patient questions, summarize clinical notes, and support appointment workflows. Implemented PHI-safe data pipelines (Spark/Hadoop/Kafka) with automated scrubbing, dataset versioning, and audit logs, and runs the system on Docker/Kubernetes with Pinecone vector search while partnering closely with clinical operations staff.”

“Strategic enterprise Customer Success leader from Atlassian Cloud managing a >$10M ARR, ~33k-user account end-to-end, driving measurable adoption (+12%), services expansion (+30%), and strong satisfaction (4.5/5). Experienced leading cross-functional deployments of AI agents (Rovo) and Forge-based integrations, and translating enterprise governance needs (e.g., RBAC at scale) into roadmap-shaping product requirements.”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

“Built and deployed a healthcare NLP application that used an LLM-style physician interface feeding a random forest model to predict treatment plans for hard-to-triage patient subgroups, backed by a Databricks medallion pipeline and heavy feature engineering to address missing/low-integrity data across ~50K patients. Also delivered an earlier Microsoft AI Builder automation that improved transportation bill payment workflows by training non-technical payroll/procurement teams to use automated outstanding-payables reporting.”

“Engineering leader with experience building point-of-care diagnostics platforms (IoT-connected PCR device delivering results in <15 minutes) and scaling multidisciplinary teams (55+). Has led major data/IoT architecture decisions (multi-cluster Kubernetes with secure routing; Kafka + Gobblin over MQTT) and runs execution with Agile roadmaps tightly aligned to GTM and senior leadership.”

“Enterprise account leader at Amazon owning the Samsung relationship end-to-end across PCs, tablets, and wearables, blending sales conversion, operational execution (inventory/demand planning), and marketing investment strategy. Delivered a 15% lift in platform adoption and improved CSAT from 3/5 to 4/5 in H1 2025, while navigating complex stakeholder conflicts (finance, demand planning, customer) and driving cross-functional fixes to catalog/listing issues ahead of major launches.”

“ML/LLM infrastructure-focused engineer who built a production stateful LLM inference service that cuts latency and GPU compute for repeated/overlapping prompts via caching with correctness guardrails. Strong in Kubernetes-based deployment and reliability engineering, using A/B testing and similarity-based evaluation to quantify performance gains without sacrificing output quality.”

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

“Senior engineering leader with experience at Cisco, Amazon, and startup Shopkick, operating at high scale (e.g., Secure Web Gateway handling ~40M QPS). Known for measurable impact across reliability and cost (85% efficacy improvement; Datadog spend cut from ~$500k/month to ~$15k/month) and for leading complex platform modernization (1-year monolith-to-microservices/event-driven migration with zero customer impact) plus compatibility-focused API design that cut device onboarding from a month to a day.”

“End-to-end product engineer who built and shipped Shopify Magic, an LLM-powered product-description generator on Amazon Bedrock with RAG over a tenant-isolated vector database, achieving 50% faster content creation, sub-2s latency, and 70%+ merchant adoption. Also led a Flexport migration from a monolithic Rails app to microservices using feature flags and parallel runs, delivering zero downtime and a 60% improvement in development speed.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”

“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”

“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”