Vetted Data Science Professionals

“Sole full-stack engineer on a founding team building an AI-native construction management SaaS, where they owned the document search interface, project explorer, and ingestion pipeline end-to-end. They also shipped a multimodal conversational AI experience for social-skills practice, showing a blend of product sense, AI integration, and startup-speed execution.”

“AI Engineer Co-Op at Northeastern University who built a production Patient Persona Chat Bot to help nursing students practice clinical interactions, fine-tuning Llama 3 and integrating a LangChain + Pinecone RAG pipeline deployed on Amazon Bedrock. Emphasizes clinical accuracy and reliability with guardrails, retrieval filtering, and continuous evaluation, and also brings strong data engineering/orchestration experience (Airflow, EMR/PySpark, ADF, dbt, Databricks, Snowflake).”

Surya Danturty

Screened

Intern AI/ML Engineer specializing in computer vision and time-series forecasting

Riverside, CA0y exp

“Undergrad who built a production RAG chatbot for a messy college website using OpenAI embeddings + FAISS, overcoming hard-to-crawl/non-selectable site content and strict API budget limits. Applies information-retrieval best practices (section-based chunking with overlap, precision/recall evaluation) and reliability techniques (edge-case testing, similarity thresholds, fallback responses), and has experience scaling similar indexing work to ~300,000 Wikipedia pages.”

“Product leader and founder/co-founder type who helped build AI products from concept through pilot, including Mira, an AI-powered tourism platform that reached 60K users and 12K engagements during a Tuscany pilot. He appears especially strong at translating between executives and technical/design teams, structuring cross-functional delivery, and applying human-centered AI principles in education through a RAG-based K-12 tutoring product, TUUTOR.”

“Senior full-stack engineer with startup experience building multi-tenant B2B SaaS platforms for manufacturing and financial operations. Strongest in Python back-end development and React/TypeScript front ends, with hands-on AWS microservices, enterprise integrations like Siemens, and measurable performance gains including a 30% reduction in application load times.”

“CoreAI intern at The Home Depot who improved the Magic Apron Assistant by building a production video ingestion + RAG retrieval system for long videos (uploads and YouTube), including a graph-based retrieval module to speed up and improve relevance. Experienced with Kubernetes orchestration (HPA) and production reliability practices like caching, monitoring, regression testing, and stakeholder-driven requirements.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Software engineer with robotics and ML background (BS Software Engineering w/ Robotics minor; MS CS w/ ML minor) who built autonomy-focused student robotics projects combining RFID + camera sensing, path planning (Dijkstra), and fuzzy logic, and experimented with neural-network approaches. Also brings production-grade software practices from a Dell software analyst role, emphasizing maintainability, documentation, and testing for real-time systems.”

“Has worked at a startup within an incubator program and is a certified mentor with a startup mentorship non-profit focused on early-stage/pre-funded companies. Not currently seeking to found a company or take equity-only roles, but open to working at very early-stage companies (pre-launch/pre-revenue/pre-funded).”

Pooja Miryala

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for banking and healthcare

Ohio, USA4y exp

“Deployed a real-time LLM-driven call center summarization and agent-assist platform at Fifth Third Bank, combining transformer models (BERT/GPT) with FastAPI inference on AKS and vector storage (ChromaDB/PostgreSQL). Emphasizes production-grade reliability (autoscaling, CI/CD, monitoring) and measurable evaluation (A/B testing), and translates model outputs into business-facing Power BI insights for call center leadership.”

Patrick Seeman

Screened

Mid-level Data Scientist and Game Tech Leader specializing in ML, healthcare analytics, and Unity

Manila, Philippines5y exp

“Data scientist at Cleveland Clinic Taussig Cancer Institute who led a production automation to convert unstructured (and sometimes image-based) pathology reports into structured data for government reporting. Built an on-prem LangGraph + Ollama pipeline with OCR (Tesseract), spell-checking, confidence scoring, and human-audited guardrails to mitigate hallucinations and improve reliability under PHI constraints.”

Varun Sharma

Screened

Mid-level AI Builder and Data Engineer specializing in GenAI and data pipelines

Remote, USA4y exp

“Full-stack AI product engineer who personally built ViGenAir, a multimodal system that turns long-form video into ads using FastAPI, React, and agentic scoring. Stands out for handling complex 50GB+ media pipelines, re-architecting systems to eliminate OOM failures, and making opaque AI workflows usable through interactive visual UX that improved trust, speed, and retention.”

“Senior product leader who helped transform Submittable from a single-program grant tool into a multi-program impact platform, driving ARR from $20M to $70M+ while improving retention and margins. Particularly strong in enterprise platform strategy and human-centered AI, with a clear philosophy of using AI to augment expert judgment rather than replace it.”

“Software engineer and University of Chicago graduate teaching assistant who built a full-stack internal analytics dashboard (React/TypeScript + Node/Express) and worked in RabbitMQ-based microservices with Prometheus/Grafana observability. Also created an AI-powered ERD diagram generator (React + MermaidJS + OpenAI) adopted by students to save hours on database assignments, using validation loops to ensure valid Mermaid output.”

“Built and deployed a production LLM-powered email assistant (“wood flow”) for a local pet resort to automate after-hours inbound email handling, including email categorization and context-aware auto-responses. Uses n8n for orchestration and applies CRISP-DM, load/edge-case testing, and RAG-based context retrieval, and has experience presenting AI solutions with budgeting and ROI to a non-technical founder.”

“AAA sports game QA tester who supported full development through launch and live updates, owning gameplay stability/regression risk. Experienced in triage-driven prioritization and in diagnosing complex crash issues (including thread synchronization) using evidence-backed Jira reports, then hardening coverage with stress/concurrency/soak and CI-integrated regression suites.”

“Application/security-focused customer-facing implementer who has secured multi-customer data aggregation apps using per-tenant isolation, short-lived/scoped tokens, and vault-based secrets management. Troubleshoots production issues via API gateway logs and performance tuning, and runs repeatable onboarding playbooks with strong customer-specific and cross-project documentation. Emphasizes AWS least-privilege IAM and secure agent deployment patterns, plus container scanning practices that catch vulnerabilities pre-production and build developer trust.”

Seymour Douglas

Screened

Executive Data & AI Leader specializing in cloud-native platforms and data-intensive systems

18y exp

“Data/ML and product leader with large-scale consumer and enterprise experience (including Walmart) who blends hands-on prototyping with executive stakeholder alignment. Has delivered measurable outcomes across personalization, semantic search/knowledge graphs, and fraud/security architecture, and has scaled organizations rapidly (30→180 in 12 months) by upskilling and building modern data/ML engineering capabilities.”

“Entrepreneurial product builder who created chaintrigger.com in response to early Web3 demand for real-time on-chain event reactions, offering an alternative to The Graph Protocol and achieving adoption among games and other Web3 projects. Currently developing new tools, dogfooding internally, and building distribution via personal network and a niche Twitter/X following to gather feedback and iterate quickly.”