Vetted Prompt Engineering Professionals

Pre-screened and vetted.

SD

Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG

Austin, TX5y exp
Tempus AILamar University
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PR

Mid-level Full-Stack Engineer specializing in AI platforms and FinTech

San Francisco, CA4y exp
StripeSaint Louis University
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HA

Entry-level Software Engineer specializing in AI/ML infrastructure

San Jose, CA1y exp
SplunkGeorgia Tech
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MK

Mid-level Business Analyst specializing in FinTech, logistics, and data analytics

USA3y exp
CopartDuke University
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AV

Junior Software Engineer specializing in AI/LLM systems

Bellevue, WA2y exp
MetaUSC
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JS

Senior Data Scientist specializing in Generative AI and conversational AI

Chicago, IL12y exp
MozillaUniversity of Michigan
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VP

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

Mountain View, CA5y exp
MetaUniversity of North Carolina at Charlotte
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YP

Senior Full-Stack & AI/ML Engineer specializing in FinTech and Healthcare IT

Remote12y exp
SnykUC Berkeley
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IL

Director-level Sales Leader specializing in cloud, AI/ML, and Enterprise SaaS

Arlington, VA10y exp
AmazonGeorge Mason University
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MV

Michael Vance

Screened ReferencesStrong rec.

Senior AI & Data Engineering Manager specializing in Appian and cloud data platforms

New York, NY10y exp
DeloitteUniversity of Virginia

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.

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JP

James Prolizo

Screened ReferencesStrong rec.

Executive Technology Leader specializing in digital, AI, cloud, and cybersecurity transformation

Atlanta, GA9y exp
SovosMercer University

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.

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Venu Dave - Junior Software Development Engineer specializing in backend data platforms and LLM applications in New York, NY

Venu Dave

Screened ReferencesStrong rec.

Junior Software Development Engineer specializing in backend data platforms and LLM applications

New York, NY3y exp
AmazonNortheastern University

Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.

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Andrew Fares - Mid-level Data Engineer specializing in AI, GenAI, and cloud data platforms in Seattle, WA

Andrew Fares

Screened ReferencesStrong rec.

Mid-level Data Engineer specializing in AI, GenAI, and cloud data platforms

Seattle, WA4y exp
AmazonMaryville University

Built production AI systems inside AWS finance/procurement, including an LLM-based supplier quote classification and price-vetting workflow that drove $5M in savings over 3 months. Combines GenAI evaluation expertise, internal platform design, and reusable Python data-quality tooling with strong cross-functional execution across finance, accounting, and hardware engineering.

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TG

Tanu Gupta

Screened

Senior Product Manager specializing in FinTech, E-commerce, and AI

London, United Kingdom8y exp
MercorCambridge Judge Business School

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.

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AR

Ali Rezafard

Screened

Director of Engineering specializing in cybersecurity SaaS platforms and cloud-scale backend systems

San Francisco, CA14y exp
ProofpointUniversity of Waterloo

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.

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GK

Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning

San Francisco, CA5y exp
MetaUniversity of Central Missouri

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.

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RS

Rajan Souda

Screened

Mid-level AI Engineer specializing in Generative AI and MLOps

St. Louis, MO6y exp
BJC HealthCareNorthwest Missouri State University

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.

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Osvaldo Calles - Senior Software Engineer specializing in developer tools, cloud automation, and generative AI in Redmond, WA

Senior Software Engineer specializing in developer tools, cloud automation, and generative AI

Redmond, WA13y exp
AmdocsUniversidad Autónoma de Guadalajara

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.

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Rahil Khan - Executive technology leader specializing in digital transformation, AI, 5G, and telecom platforms in Frisco, TX

Rahil Khan

Screened

Executive technology leader specializing in digital transformation, AI, 5G, and telecom platforms

Frisco, TX26y exp
SterdyneUniversity of Illinois Urbana-Champaign

Founder of a cloud-based TV sharing startup who has already raised angel and Series A funding, then pitched larger VCs in New York and Silicon Valley using an MVP and live demos. Demonstrates practical knowledge of the VC and accelerator landscape, stage-appropriate fundraising, and capital-efficient startup building.

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Jared Hoffen - Senior AI Engineer specializing in LLM agents, RAG, and ML infrastructure in Las Vegas, USA

Jared Hoffen

Screened

Senior AI Engineer specializing in LLM agents, RAG, and ML infrastructure

Las Vegas, USA12y exp
AI Research LabCalifornia State University, Northridge

Production-focused AI/ML engineer who has owned LLM agent and RAG systems end-to-end, from experimentation through deployment, monitoring, and iterative optimization. Stands out for building evaluation and observability layers around GenAI systems and delivering measurable gains in task success, regression detection speed, and token efficiency in production.

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