Vetted Prompt Engineering Professionals

Asrith Velireddy

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems

Harrison, NJ4y exp

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

“Built GenMedX, a multi-module clinical AI system for emergency department decision support spanning triage prediction, diagnosis, medication Q&A, and visit summarization. Stands out for combining medical LLM fine-tuning, RAG, and rigorous evaluation/monitoring to drive a major triage recall improvement from 38.5% to 76.6%, with a strong focus on safety, edge-case detection, and production reliability.”

“Technical lead who shipped a production GPT-4-powered customer support agent for Square, serving a large fintech customer base through a React chat interface with tool-using orchestration, guardrails, and live handoff paths. Brings strong real-world experience in agent reliability, evaluation, observability, and workflow orchestration using Temporal, Sidekiq, Pinecone, Datadog, and Snowflake.”

“AI product leader with end-to-end experience building revenue-generating products and internal AI platforms across legal tech, edtech, and neurofeedback healthcare. At Clio, they helped launch the company's first AI offerings, drove $60K MRR in two months, scaled the AI team from 15 to 45, and enabled 12 additional AI applications across the business. They also bring a strong human-centered AI perspective from building tutor matching, lesson-planning, and clinician recommendation systems.”

“AWS engineer who productionized an internal ML-driven data pipeline from a notebook prototype into a scalable, observable Python service (schema validation, deduplication, idempotency, safe retries, versioned transforms, CloudWatch alarms), reducing manual effort and improving data accuracy/trust. Experienced diagnosing workflow issues in real time (e.g., upstream schema changes) and partnering with account managers/support to unblock adoption of seller-facing Marketplace features by demonstrating reliability with concrete metrics.”

“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”

“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”

Tejaswini Manjunatha

Screened

Mid-level Reliability Engineer specializing in incident response and LLM-driven support automation

Palo Alto, USA4y exp

“Customer Success Services / Support professional working on Palantir Foundry who productionizes customer integrations (secure OAuth2, scheduled pipelines) and builds LLM-driven support automation (runbook matching) with monitoring and evaluation suites. Also leads developer workshops/demos on Foundry packaging/installation workflows, using live debugging techniques to make concepts concrete.”

Anushka Bhagchandani

Screened

Intern Product & Software Engineer specializing in GenAI/LLM and e-commerce platforms

San Francisco, CA2y exp

“Software engineer (2+ years in India) and current GenAI intern who shipped LLM-powered review-writing enhancements at Myntra (Walmart-backed), using pilots and A/B tests to lift review quality by 5% in 30 days. Demonstrates strong LLM operations discipline (logging, dashboards, alerts, rollback) and fast incident response, plus experience delivering developer-focused workshops and public technical talks.”

Ranjani Salla

Screened

Mid-level AI/ML Engineer specializing in LLMs, FinTech, and Healthcare IT

USA5y exp

“Built production GenAI systems in both healthcare and financial services, including a Verily clinical platform and an Accenture financial Q&A product. Stands out for combining advanced RAG, fine-tuning, safety evaluation, and infrastructure engineering to deliver measurable gains in engagement, groundedness, hallucination reduction, and cost efficiency.”

“Built multiple AI-powered products from scratch, including ConnectAbility, an accessibility tool combining computer vision and LLMs to describe visual content for users with disabilities, and SpamBack!, a macOS app that detects scam texts and auto-generates responses. Stands out for full-stack/backend ownership of applied AI systems, especially around async workflows, inference performance, and reliability safeguards.”

“Candidate describes a development approach centered on AI-assisted coding, testing, and agent-driven workflows, including production exposure to multi-agent systems and governance-oriented logging. They appear particularly focused on combining AI speed with structured validation through unit tests, boundary tests, and edge-case monitoring.”

“UI/UX and product-minded designer with experience shaping digital products from concept to launch across telecom and higher education. Notably pitched a new Globe Telecom app that became the basis for the product after joining, helped launch it within five months, and contributed to a 4x subscription lift through A/B-tested in-flow upsell design.”

Yashwanth J

Screened

Mid-level Software Engineer specializing in AI/ML and full-stack systems

Seattle, WA4y exp

“Engineer with Apple experience building LLM-powered internal workflow orchestration systems using Python, LangGraph, FastAPI, Redis, vector search, and Kubernetes. Stands out for a highly pragmatic, production-focused approach to agentic systems: deterministic state management, strong guardrails, observability, and human review for high-risk actions.”

“AI/LLM-focused engineer with hands-on experience building RAG pipelines, prompt engineering workflows, and multi-agent systems using tools like LangChain. Stands out for combining AI-assisted development with production-grade validation and for leading the architecture/orchestration of agent-based recommendation systems that improved response time, accuracy, and scalability.”

“Built and productionized a dual-agent LLM invoice-processing system for GFI Partners, adding guardrails and audit trails to earn stakeholder trust and drive adoption while cutting operational burden by 75%. Uses LangSmith observability to diagnose real-time workflow regressions and has experience teaching agentic AI concepts (e.g., at Carnegie Mellon) through hands-on, scaffolded demos.”

“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”

“Unity/C# gameplay engineer who has shipped across console/PC/VR and mobile, including Ace Combat 7 (VR) and mobile titles. Built monetization-impacting gameplay systems (power-ups) and a full server-authoritative multiplayer architecture, and uses LLM-driven editor tooling and multi-agent workflows to rapidly prototype features while adding automated testing, benchmarking, dashboards, and CI/CD.”

greg farhadian

Screened

Senior Software Engineer specializing in cloud data platforms and Java microservices

Remote4y exp

“Backend/data engineer with experience building Kafka-driven real-time pipelines that support ML code deployment and downstream integrations. Currently migrating high-throughput mainframe (COBOL/assembly) processing to Java, using Spark/Databricks to preserve performance and employing rigorous A/B testing across dev/pre-prod/prod with years of historical data.”

Yash Jajoo

Screened

Senior Software Engineer specializing in AI and FinTech platforms

New York City, NY8y exp

“Built a production LLM pipeline at Walter AI that scans massive user inboxes, identifies financial newsletters, and extracts trading strategies into structured JSON for downstream paper-trading workflows. Stands out for combining agent architecture with strong production discipline—cutting scan time from 20 to 5 minutes, reducing LLM costs by 90%, and achieving 3-second P99 latency while handling messy, inconsistent email data at scale.”

Xingyu Chen

Screened

Junior Machine Learning Engineer specializing in generative AI and computer vision

Atlanta, GA1y exp

“Built production AI features for image editing and object removal, including an agent that guides users to the right pipeline, validates inputs, refines prompts, and routes requests to GPU-backed generation services. Brings hands-on experience across multimodal control, generative model optimization, and post-launch iteration driven by failure analysis and user feedback.”

“Built both traditional data infrastructure and LLM-powered product workflows, spanning a Python/SQL ETL deployment at Amazon and an adaptive learning system for their DataLingo platform. Particularly interesting for roles at the intersection of data engineering, applied AI, and customer-facing product delivery, with hands-on experience stabilizing probabilistic LLM systems in production.”

“Software engineer with practical, day-to-day experience embedding AI into development workflows across coding, testing, code review, and AWS data pipelines. Uses tools like Claude, Cline, JUnit, Mockito, and Amazon Bedrock, and stands out for having a realistic, mature view of agent limitations, hallucinations, and the need for strong prompting and human validation.”