Vetted Prompt Engineering Professionals

Kasireddy Kumar reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare, fraud detection, and recommender systems

Missouri, USA6y exp

“Healthcare-focused applied ML/LLM engineer who has deployed production systems including an LLM medical documentation assistant that summarizes unstructured EHR notes into physician-ready structured outputs. Experienced building secure, compliant pipelines (PHI minimization, RBAC, encryption) and scaling via Docker/Kubernetes/Azure ML, plus orchestrating ETL/ML workflows with Airflow and Kubeflow; also built an LLM-driven clinical coding assistant at Centene with measurable performance metrics.”

Vikram Kini

Screened

Mid-Level Full-Stack Engineer specializing in React, TypeScript, and microservices

3y exp

“Built and productionized an AI agent-based in-app assistant at ISAFE to guide users through document workflows, piloting with a partner school district and then rolling out across districts. Combines hands-on LLM/agent debugging (logs, fallback rates, state/context tracking) with strong technical demos and sales enablement through live workflows and pilot programs (e.g., Osceola School District).”

Akshay Danthi

Screened

Senior AI Engineer specializing in production GenAI systems

San Francisco, CA8y exp

“AI engineer who has shipped production LLM systems end-to-end, including a natural-language-to-SQL analytics copilot for career advisors that achieved ~95% query success through schema grounding, access controls, and automated regression testing with golden queries. Also builds LangGraph-orchestrated multi-step agents (resume analysis, recommendations) and RAG pipelines (PDF ingestion + FAISS) and partners closely with non-technical users to drive adoption and trust.”

Dylan Zhu

Screened

Mid-level Machine Learning Engineer specializing in computer vision and generative AI

Hoboken, NJ7y exp

“Built and deployed an LLM/RAG system that uses differential privacy and distributional similarity checks to transform private data into a non-sensitive knowledge base while preserving utility. Also has experience demonstrating adversarial ML concepts (FGSM) to non-technical audiences by focusing on observable model behavior rather than implementation details.”

“Built and shipped LLM-powered agents including a production RAG research assistant that cut research lookup time from ~20 minutes to ~10–20 seconds using caching, retrieval thresholds, and citation-enforced grounded answers. Also designed multi-step, tool-calling workflows with stateful critique/revision loops and pragmatic monitoring (retry/schema-failure/low-confidence signals) plus normalization/validation layers for messy notes/spreadsheet-style data.”

“Cloud-focused platform/infrastructure engineer centered on AWS and Kubernetes, with hands-on experience building EKS environments via Terraform/Terragrunt, bootstrapping clusters with Cilium and ArgoCD, and integrating persistent storage and backup tooling like EFS/EBS CSI and Velero. While no longer focused on on-prem or hybrid operations, they also bring prior VMware/Hyper-V and migration-to-AWS experience, making them strongest in modern cloud platform engineering roles.”

Priyanshi Sharma

Screened

Mid-level Software Engineer specializing in FinTech full-stack and backend systems

Parsippany, NJ4y exp

“Built and productionized a GenAI prompt-engineering solution to retrieve prevailing wages based on job/location selections, emphasizing accuracy through stricter prompt templates and validation. Hands-on in real-time production debugging using Splunk (callback tracing, verbose logging, header inspection) and experienced running developer-facing demos/workshops that helped drive marketplace API adoption.”

Pavan Punna

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and healthcare-fintech AI

Dallas, TX5y exp

“Built and owned a production GPT-4 RAG assistant for clinical and enterprise query resolution, taking it from initial experiment to deployment, monitoring, and iterative improvement. Their work cut resolution time from 45 minutes to under 2 minutes, achieved roughly 95% accuracy, and scaled to thousands of additional monthly queries while emphasizing safety and trust in a sensitive clinical domain.”

“ML/AI engineer with production experience at S&P Global and Accenture, focused on regulated, enterprise-grade systems. Built end-to-end financial risk and credit default models with >90% precision and 12% fewer false positives, and is currently developing secure RAG pipelines on AWS SageMaker for enterprise insight extraction.”

“ML/AI engineer at PNC who has shipped enterprise-grade RAG and document intelligence systems for compliance and policy workflows. Stands out for combining LLM product thinking with production rigor—owning FastAPI/Kubernetes deployments, monitoring, evaluation, and human-feedback loops that drove measurable gains like 40% faster policy search and 30% faster compliance review.”

premkumar narla

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG, and enterprise ML systems

Chicago, IL5y exp

“ML/AI engineer with hands-on experience at Morgan Stanley building production fraud detection and enterprise RAG systems. Stands out for owning systems end-to-end—from experimentation and deployment to monitoring and iteration—and for delivering measurable impact, including an 18% reduction in fraud false positives, 40% lower inference latency, and internal tooling that reduced model deployment time from days to hours.”

ASHISH DONDAPATI

Screened

Mid-level AI/ML Engineer specializing in Generative AI for Financial Services

San Francisco, USA6y exp

“ML/AI engineer with strong financial-services domain experience who has built production systems spanning trade anomaly detection, investment-research RAG, and agentic LLM workflows. Particularly compelling for teams needing someone who can take ML/GenAI from prototype to monitored production while balancing compliance, latency, cost, and reliability.”

“Built and owned an enterprise LLM/RAG document intelligence platform for PNC Financial Services in a compliance-heavy environment, focused on grounded answers over internal finance and policy documents. Stands out for combining GenAI product delivery with production engineering discipline, delivering 60% faster document review and materially better answer quality while creating reusable FastAPI-based AI services for multiple teams.”

Sangita Satapathy

Screened

Senior Full-Stack Engineer specializing in FinTech and AI applications

Las Vegas, NV11y exp

“Engineer with a pragmatic, production-focused approach to AI development, using tools like Copilot and ChatGPT to accelerate coding while maintaining strong engineering fundamentals. Has led a RAG-based multi-stage AI solution spanning retrieval, context building, and response generation, with an emphasis on validation, prompt quality, and reliability.”

“Software developer with a one-year Philips co-op who has already applied AI-assisted coding in production, not just side projects. Stands out for using multi-agent development setups with task-specific sub-agents and a clear human-led orchestration philosophy focused on context, quality control, and security.”

“Built Nexthire-AI, shipping an end-to-end LLM-powered resume–job description matching product (React + Node.js) using embeddings and retrieval to generate match scores and skill-gap recommendations. Improved post-launch engagement by making feedback cleaner and more actionable, and added production guardrails (validation, timeouts, fallbacks) to handle messy resume formats and AI API instability.”

Omkar Lahurikar

Screened

Senior engineering founder specializing in AI systems and automotive controls

Dearborn, MI13y exp

“Builder-minded founder/operator currently at Ford who previously co-founded MeruDynamics, an "Uber for manufacturing" platform, and took it from zero to paying customer without raising capital. He also independently built and deployed RDD, an AI copilot for engineering decision capture, with evals, prompt versioning, and LLM cost telemetry—showing unusual depth in both product thinking and practical AI execution.”

Siva Harini Sri Janaki Raman

Screened

Mid-level Data Engineer specializing in cloud data platforms

Dallas, TX3y exp

“Built an AI-powered internal support assistant at CVS Health using GPT-4, LangChain, and Pinecone, applying RAG, validation, and monitoring to reduce repetitive support tickets while protecting sensitive healthcare data. Stands out for a pragmatic approach to AI engineering: using multi-agent and LLM workflows to accelerate development while keeping systems constrained, observable, and production-friendly.”

“LLM/agentic systems practitioner who specializes in moving demo-only assistants into reliable, observable, cost-controlled production services. Strong in real-time diagnosis of complex agent workflows (including tracing, loop detection, and guardrails) and in customer-facing enablement—running workshops, building tailored PoCs, and partnering with sales to close deals by proving reliability in high-risk pilots.”

“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”

Anudeep Eloori

Screened

Mid-level Software Developer specializing in full-stack enterprise applications

USA3y exp

“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”

Anuja Nayak

Screened

Junior Software Engineer specializing in distributed systems and reliability

Seattle, WA3y exp

“Oracle engineer focused on reliability and internal platform tooling, with hands-on experience automating regional traffic failover and building an LLM-assisted incident investigation workflow. Stands out for owning production-impacting systems end-to-end and delivering measurable operational gains, including cutting failover recovery to under five minutes and reducing incident triage from hours to minutes.”

“Full-stack engineer with experience shipping analytics dashboards and an AI-driven support assistant for a cloud analytics platform. They combine Java/Spring Boot backend work with TypeScript frontend development and showed practical knowledge of LLM production concerns like retrieval grounding, latency, caching, retries, and graceful fallbacks. Their shipped dashboard feature improved load times by 35-40% and reduced support issues tied to delayed analytics.”

“AI automations intern at a lean US-based marketing agency who works directly with founders and builds practical GTM systems end-to-end. He combines ML/LLM tooling with outbound execution, including a clustering-based recommender that improved client lead generation by 30% in two weeks and a personal cold outreach engine that achieved a 12%+ reply rate.”