Vetted Prompt Engineering Professionals

Yukta Chikate

Screened

Mid-level Machine Learning Engineer specializing in safety-critical and uncertainty-aware ML systems

Brooklyn, NY5y exp

“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”

Rohan Varma Bandari

Screened

Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG

USA4y exp

“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

Dwarakesh Rajesh

Screened

Intern Robotics/Mechatronics Engineer specializing in automation and ROS2 systems

Boston, MA1y exp

“Robotics software builder who developed a solo fault-adaptive robotic arm: current-based joint health monitoring feeding an ESP32, ROS 2/MoveIt 2 motion planning that adapts to joint failures, and a custom brute-force IK solver to overcome URDF/MoveIt singularity issues. Also worked on real-time 12-microphone sensor-fusion audio processing for drone navigation, resolving buffer/noise problems with multithreading and chunked sampling; experienced with Webots/CoppeliaSim and learning Isaac Sim.”

“Built end-to-end internal and user-facing automation/data features, including a Selenium-based BU course scraper with around 1,800 users and a CSV export system that became the company standard at Triple. Shows initiative in ambiguous environments, working directly with business stakeholders and resolving production infrastructure issues involving AWS and Terraform.”

Adhiraj Chadha

Screened

Junior Software Engineer specializing in AI, data, and full-stack applications

Amherst, MA3y exp

“Builder with a mix of backend engineering, product instinct, and startup execution: they shipped a legal BI platform from scratch that handled 1,000+ cases, cut reporting time 80%, and saved $30K annually. They also move quickly in ambiguous environments, from launching a roommate app across iOS/Android after user discovery to building a RAG system with a 50+ case evaluation suite and a cloud dev environment in under 48 hours.”

“Engineer with hands-on experience designing and leading multi-agent AI development workflows, including a LangGraph-based system that automated parts of a RAG pipeline and significantly reduced development time. Stands out for treating AI agents like an engineering team, with clear architecture, handoff schemas, validation, and supervisor-driven conflict resolution.”

“Backend engineer who built an AI-powered grant matchmaking platform for researchers and professors, combining semantic matching, embeddings, and Semantic Scholar enrichment with rule-based eligibility filters. Stands out for pragmatic AI engineering: they focused on reliability through confidence scoring, logging, manual validation, and production-minded backend design.”

varunkumar jukanti

Screened

Senior Front-End Engineer specializing in enterprise UI architecture for FinTech

Jersey City, NJ7y exp

“Senior frontend engineer with 6 years of experience building enterprise-grade Angular/React applications in regulated industries, especially banking. Most notably worked on BLCM at Tasha Bank, a compliance platform that replaced fragmented Word/Excel SharePoint processes with a unified browser-based workflow for analysts and managers, and drove measurable performance and usability gains.”

“Front-end engineer with strong ownership of complex browser-based trading platforms for portfolio managers and traders. Stands out for building high-performance React/Redux UIs that handle real-time market data and large datasets, while also improving expert-user workflows through thoughtful usability enhancements.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Software engineer/product owner who has led end-to-end delivery of AI and content-management platforms, including building RAG-based reliability improvements and migrating fragile systems to containerized AWS ECS/Kubernetes with Terraform-managed CI/CD. Experienced designing event-driven microservices (SQS/SNS/RabbitMQ), scaling queue consumers with autoscaling, and creating internal Python tooling to standardize data connectors (e.g., BigQuery/Airtable/internal APIs) to speed iteration.”

“Built and shipped a production RAG-based enterprise knowledge assistant to replace slow/inaccurate search across millions of documents, using LangChain orchestration with GPT-4/LLaMA and vector databases. Strong focus on production constraints—latency, hallucination control, and cost—using hybrid retrieval, guardrails, LLM-as-judge validation, and model routing, and has experience translating non-technical stakeholder pain points into measurable outcomes.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”

“Built and productionized an internal LLM-powered search tool that helps engineers find the right SolidWorks macros using plain-English queries, using OpenAI embeddings and ChromaDB with strong logging/fallback safeguards. Experienced in diagnosing RAG/agentic workflow issues in real time and in hands-on API support, including fixing customer macros after SolidWorks version updates and driving adoption through reusable solutions and best practices.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Built and led deployment of an autonomous, self-correcting multi-agent knowledge retrieval and validation system at HCA Healthcare to reduce heavy manual research/validation in clinical/compliance documentation. Deeply focused on production reliability and cost—used LangGraph StateGraph orchestration plus ONNX/CUDA/quantization to cut GPU costs by 25%, and partnered with the Compliance VP using real-time contradiction-rate dashboards to hit a 40% automation goal without compromising compliance.”

“Full-stack engineer with recent Mutual of Omaha experience building a cloud-native microservices application in Java/Spring Boot with a React/Angular frontend, integrating multiple AWS services (Lambda, S3, DynamoDB, SQS). Has hands-on experience operationalizing AI features via OpenAI/AWS Bedrock and improving reliability/performance through caching, async processing, and CI/CD pipeline optimization.”

“Full-stack engineer focused on productionizing LLM applications, including an Android privacy-policy risk summarization app (Kotlin/React Native + FastAPI + Ollama) that cut response times from ~10s to ~5–6s via batching, caching, async, and event-driven architecture. Currently at PRGX building an LLM-based legal contract clause extraction system, partnering closely with legal/procurement SMEs to create schemas, labeled datasets, and evaluation pipelines that improved accuracy from 70% to 85%. Also has experience architecting real-time voice/LLM systems with streaming microservices (Kafka, Kubernetes, gRPC/WebSockets) and an avatar chatbot pipeline (TalkingHead, Google TTS, AnythingLLM).”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“Software engineer who has built both internal developer productivity tooling (a backend API supporting repeatable UI test data/mocking for Dapper) and a personal Go-based LLM workout coach using Gemini and structured logs/config. Emphasizes maintainability and reliability via scalable UI component tagging (Telerik), audit logs, and reproducible Dockerized environments; targeting $160k base.”

“Built production LLM tooling focused on reproducibility and verification by enforcing JSON schemas and using multi-step checks with tools like Firecrawl and Perplexity. Also implemented the containerized infrastructure layer for a 9-agent app on K3s, dealing with rolling updates and uptime, and has experience advising a non-technical builder on search grounding and LLM data-flow design.”