Vetted pandas Professionals

“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”

Prasad Sadineni

Screened

Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

Nashville, TN6y exp

“Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).”

Dhairya Shah

Screened

Entry-level Machine Learning Engineer specializing in computer vision and systems

Buffalo, NY1y exp

“ML-focused builder who has shipped an end-to-end income-class prediction product: built the data pipeline, trained models, deployed via Streamlit with a live UI, and tracked success via accuracy (84%), adoption, and latency. Demonstrates strong practical MLOps instincts (Docker/Streamlit Cloud, logging/monitoring, caching) and data engineering reliability patterns (schema checks, idempotency, retries, backfills) while iterating quickly in ambiguous, solo-project environments.”

Lando Ringel

Screened

Senior Unity Developer specializing in AR/VR, AI, and game systems

Melbourne, FL11y exp

“Game developer with a distinctive niche in integrating LLM-driven dialogue directly into Unity gameplay, including a full web-hosted architecture using llama.cpp, FastAPI, and cloud GPUs for real-time NPC interaction. Also brings VR/mobile optimization experience from a drone simulation project at GovScio, combining AI systems thinking with practical 3D environment and performance work.”

Yeruva Bala Shreya Reddy

Screened

Entry Data Analyst specializing in ETL pipelines and business intelligence

Charlotte, NC0y exp

“Analytics candidate with hands-on experience building reliable healthcare reporting layers from messy transactional data using SQL and Python. Stands out for combining data transformation, KPI definition, validation rigor, and performance tuning to deliver reusable reporting assets that improve trust in operational metrics.”

Shehab mohamed mohamed

Screened

Junior AI Engineer specializing in MLOps, LLMs, and multi-agent systems

Cairo, Egypt2y exp

“ML/AI engineer focused on production-grade systems, with experience building a low-latency multi-agent 'neural concierge' booking platform used across domains like restaurants and hospitals. Also worked on a healthcare computer vision system for nystagmus/eye-movement analysis, showing a mix of scalable LLM infrastructure, MLOps, and safety-conscious medical AI experience.”

“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”

“Systems-focused C++ engineer who built a 32-bit CPU simulator end-to-end (custom ISA, full memory model, fetch-decode-execute loop) and solved tricky recursion/stack-frame correctness issues through heavy instrumentation and tracing. Has strong Linux and user-kernel boundary experience (procfs) plus modern build/test tooling (Docker, CI/CD, pytest), and is confident ramping quickly into ROS/ROS2 despite not having used it directly.”

“Built and productionized LLM-powered workflows that generate contextual insights from structured financial data, including prompt/retrieval design, data standardization, and reliability controls like rate limiting and batching. Also diagnosed and fixed real-time failures in an automated order validation system using logs/metrics, staging reproduction, edge-case handling, retries, and alerting, while supporting sales/customer teams with demos, scripts, and FAQs to drive adoption.”

“Built and shipped a production LLM feature that explains DSA problems with real-life explanations, using Grok with automatic failover to OpenRouter (and multiple backup models) to avoid user-facing failures. Improved cost efficiency by implementing difficulty-based token budgets and iterated prompt quality via structured constraints and an in-app feedback mechanism, reporting satisfaction across 38 users.”

“Built and productionized an LLM-powered service desk ticket triage and reporting agent that classifies, prioritizes (including sentiment/urgency), and summarizes tickets into structured SQL outputs feeding Power BI dashboards. Emphasizes production reliability (99% uptime) with retries, schema validation, confidence thresholds, human review queues, and rule-based fallbacks, delivering 85–90% reduction in manual effort and 25–30% faster resolution times.”

“Data engineer focused on building reliable, production-grade pipelines and data services end-to-end, including a 50+ GB/day pipeline ingesting from APIs/files into Snowflake with PySpark/SQL transformations. Emphasizes strong data quality controls, monitoring/retries, and performance optimization, and has also shipped a Python data API with caching and backward-compatible versioning.”

Prasanth Kumar Korada

Screened

Mid-level QA Automation Engineer specializing in Playwright and cross-browser E2E testing

3y exp

“QA automation engineer with strong end-to-end ownership of UI automation for financial transaction workflows, using Selenium/Playwright/Cypress and CI/CD gating. Improved suite robustness via UI-API validations, negative testing, and flake reduction (intercepts + data-testid), catching critical backend calculation issues before production and cutting regression runtime by 40%.”

Iskhak Ishmakhametov

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and real-time systems

Bellevue, WA7y exp

“Full-stack product engineer with a strong real-time systems focus: built and rolled out a WebSocket-based notifications system (with robust reconnect/resync and event ordering protections) that cut update latency to under 200ms. Also owned a workflow automation platform backend in FastAPI (JWT/RBAC, versioned APIs, standardized errors), designed the PostgreSQL schema for workflows/tasks/executions, and operated deployments on AWS ECS Fargate with blue-green CI/CD and performance stabilization via caching and autoscaling.”

Habtom Desta

Screened

Mid-level Data Scientist specializing in Python, ML, and BI dashboards

Dallas, TX5y exp

“Data/NLP practitioner who builds production-oriented pipelines for unstructured text: entity extraction, topic modeling (LDA/BERTopic), and semantic search using Sentence-BERT embeddings with FAISS. Emphasizes rigorous evaluation (coherence/silhouette + manual review), entity resolution with validation, and scalable workflow orchestration using Airflow/Prefect with Spark/Dask.”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”

“Backend/integration-focused software engineer who built and debugged a complex location modeling system (data pipelines, APIs, optimization logic connected to a dashboard). No direct ROS/robotics experience yet, but demonstrates strong distributed-systems debugging, containerized deployment (Docker), and CI/CD/testing practices and is actively looking to pivot into robotics software.”

“Backend engineer with production ERP experience deploying microservices and improving performance/reliability using a metrics-driven approach (logs, latency, error rates). Has hands-on cloud/hybrid operations across AWS and Azure with Docker/Kubernetes, and has resolved real-world mobile sync issues by tuning timeouts/retries and reducing payload sizes. Builds configurable Python services to deliver customer-specific behavior without destabilizing the core codebase.”

“Built and deployed a production RAG-based internal document Q&A system using LangChain, vector search, and a dockerized FastAPI LLM service. Focused on reliability by systematically reducing hallucinations and improving retrieval through prompt grounding/abstention strategies, chunking and top-k tuning, and iterative evaluation with logged metrics and manual validation.”

“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”

Puspa Oli

Screened

Junior Machine Learning Engineer specializing in NLP, Computer Vision, and FinTech AI

Kathmandu, Nepal2y exp

“AI/LLM engineer who has shipped production RAG and agentic systems end-to-end (LangChain/FAISS, OpenAI+Gemini, FastAPI, Docker, Streamlit), focusing on retrieval quality and low-latency performance. Also partnered with a non-technical PM at deepNow to deliver a forecasting + summarization pipeline for daily market insights with iterative prototyping and a simple UI.”

Dhwani Patel

Screened

Mid-level Full-Stack Python Developer specializing in AI/ML and backend APIs

BC, Canada15y exp

“Python/Django backend engineer with open-source experience upgrading Archivematica to Django 4.2 LTS, including resolving a tricky breaking change in datetime parsing by implementing a preservation-safe legacy timestamp conversion layer. Also built a cost-efficient, reproducible Small Language Model (Microsoft Phi-3) fine-tuning pipeline that turns CSV product data into a domain-specific searchable Q&A chatbot, with emphasis on memory optimization and overfitting prevention.”

“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”

Yousef Abdelsalam

Screened

Junior Software Engineer specializing in Odoo, web performance, and backend systems

Orlando, FL2y exp

“Full-stack developer who shipped LLM-powered customer support automation, including an AI call center designed for always-on, high-concurrency real-time phone handling. Also built a WhatsApp lead-conversion chatbot using Zapier webhooks, Redis state, and Twilio messaging, and reports measurable outcomes (+11% customer satisfaction, ~7% cost reduction) while using GPT-4.1.”