Vetted Prompt Engineering Professionals

“Product Manager at Amazon focused on recruiting technology and AI/ML, owning ATS experiences at massive scale across 24M annual applications. Stands out for driving a vision to turn Amazon's ATS into an AI-native platform, with measurable business impact including $10M+ in savings, major recruiter efficiency gains, and strong experimentation-driven product decisions.”

“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”

“Software engineering intern who built an internal AI-agent automation using the Gemini API to reduce manual CRM data entry, iterating prompts closely with analysts to address precision concerns. Also worked on a medical image-diagnostics LLM project involving fine-tuning and benchmarking multiple model approaches, and has quant/sales-trading experience building automated pricers for complex options and persuading sales teams to adopt them with ROI-focused metrics.”

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Embodied AI/robotics-focused ML engineer who built a real-time assistive Braille device by coupling transformer OCR with an Arduino-controlled electromechanical Braille cell, solving tight latency and hardware-integration constraints. Has recent work on geometry-grounded world models and a real-time 4D reconstruction foundation model (Any4D), and delivered measurable impact at Voxel AI by building a scalable headless simulation + synthetic data pipeline that improved a safety-critical algorithm’s recall by ~16%.”

Priyanka Kaswan

Screened

Senior AI Research Engineer specializing in LLM agents and large-scale ML

7y exp

“AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.”

“Built a production LLM-powered data ingestion workflow at Provi, an online alcohol marketplace, to clean and match millions of distributor inventory items against a product catalog. Their experience is strongest in applying LLMs to real-world, large-scale data operations with AWS Glue, S3, batching, API integration, human review, and drift detection.”

“Meta engineer who has shipped both user-facing full-stack product work and internal AI agent systems at production scale. Most notably built AuditFixer, an agentic remediation pipeline that fully automated audit-fix workflows, cut turnaround from 6-7 hours to under 1 hour, and has already produced 30+ landed diffs, while also owning the Llama API evaluation flow launched for thousands of developers.”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

“Solutions/implementation-focused engineer with payments expertise (Adyen headless Magento integrations, 3DS components) who also builds and troubleshoots agentic LLM workflows using the OpenAI Agents SDK. Experienced in pre-sales technical validation and in tailoring live demos/workshops—e.g., pivoted a Quantum Metric workshop from custom JavaScript instrumentation to no-code analytics based on audience needs.”

Abhinav Bandaru

Screened

Junior Data Scientist specializing in Generative AI and agentic LLM systems

San Jose, CA1y exp

“LLM/agentic-systems builder who has shipped production tools for investment research and procurement insights, including a company screener that processes thousands of conference-listed companies using FireCrawl + Google Search + Gemini. Demonstrates strong orchestration expertise (LangGraph multi-agent graphs), performance optimization (async/batching to sub-30s), and pragmatic reliability/evaluation practices with stakeholder-friendly UX (real-time cost tracking and model/parameter toggles).”

“Engineer with startup experience spanning fintech and B2B SaaS, from Playd during rapid growth to Clarity’s AI-powered contract review platform. Particularly strong in React/TypeScript product development for complex, data-heavy workflows used by accountants and finance teams, with supporting experience in Node, Postgres, AWS, and production database design.”

“LLM/Conversational AI engineer who built a production multi-turn dialogue system using LoRA fine-tuning on LLaMA, cutting training compute/memory by 90%+ while maintaining low-latency inference via quantization and streaming generation. Experienced in orchestrating end-to-end ML workflows with Prefect/Airflow/Kubeflow (including hyperparameter sweeps and W&B tracking) and improving agent reliability through benchmark-driven testing, shadow-mode rollouts, and stakeholder-informed guardrails.”

Kieron Ong

Screened

Junior Software Engineer specializing in AI platforms and full-stack systems

New York, NY2y exp

“Frontend/product engineer with strong experience building sophisticated AI-assisted browser UIs for customer support operations in healthcare/therapy contexts. Particularly compelling for teams needing someone who can combine modern web architecture, observability, typed systems, and human-in-the-loop AI UX to improve both reliability and agent efficiency.”

“Backend/data engineer from Oscar Health specializing in healthcare claims systems on AWS. Built HIPAA-compliant real-time services (FastAPI/Postgres/Kafka on EKS) and serverless ingestion pipelines, and led modernization of a legacy SAS claims pricing system to Python/Spark with rigorous parity validation. Demonstrated measurable impact with high uptime/low latency services and major Snowflake performance and cost reductions.”

“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”

“Frontend-leaning full-stack engineer who built an internal real-time operations dashboard from 0→1 using React, TypeScript, Redux Toolkit, Material UI, and Node.js integrations. Stands out for hands-on performance tuning at scale—profiling and fixing excessive re-renders, optimizing live-update UIs, and iterating post-launch with caching, pagination, and observability.”

“Fintech and AI product engineer who has owned major production rollouts, including Lending Club's banking-arm launch, and has since built LLM-powered decision systems for finance and climate use cases. Particularly strong in combining stakeholder management with pragmatic architecture choices like observability, deterministic pipeline design, RAG, and document-to-structured-data workflows.”

Armenak Bdoian

Screened

Senior Product Leader specializing in AI-enabled marketplaces and e-commerce

Seattle, WA18y exp

“Product leader with experience spanning Amazon Prime and List.am, where they built a product org from scratch and drove a marketplace transformation for 3M users. Particularly strong in zero-to-one and rebuild work across search, ads, AI moderation, and complex cross-functional alignment, with measurable impact on engagement, revenue, and operational efficiency.”

David Gross

Screened

Entry AI Software Engineer specializing in LLM workflows and ML pipelines

Redmond, WA2y exp

“Built an autonomous-agent document indexing concept in a hackathon with Microsoft and The Seattle Times, architecting an Azure-based system (Azure AI Foundry, Cosmos DB, Azure indexing, Copilot Studio) and coordinating closely with the customer team. Also created and pitched a sports matchmaking app (Ludicon), combining user studies, feature implementation, and technical support on sales/investor calls.”