Vetted pandas Professionals

Yash Amre

Screened

Intern Data Scientist specializing in machine learning and NLP

California, USA1y exp

“Analytics-focused early-career candidate with internship experience owning reporting and system performance analysis projects end to end. They combine SQL data preparation, Python automation, and dashboard delivery with measurable impact, including roughly 50% less manual reporting and about 20% better forecast accuracy.”

“Candidate is targeting a CTO Venture Studio role and positions themself as a technical partner to founders rather than a founder personally. They demonstrate strong fluency in early-stage startup evaluation, especially around validating whether a product truly tests the business hypothesis and whether the underlying technology can scale significantly.”

“Full-stack product engineer focused on data-heavy dashboard applications, with hands-on ownership from React/TypeScript UI through Node/Express APIs to Postgres schema design and optimization. Stands out for combining product sense with engineering rigor: improving onboarding and reporting flows using analytics and user feedback, while also building reusable upload infrastructure and safe multi-tenant configurable experiences.”

Panita Vaishnavi Kanaguduru

Screened

Mid-level Business Analyst specializing in retention, churn, and revenue analytics

Remote4y exp

“Early-career data analyst with hands-on experience at SuperWorld building SQL and Python analytics pipelines for product and growth use cases. They stand out for turning messy event and transaction data into validated funnel datasets, automating reporting to cut manual effort by ~40%, and partnering with product and marketing teams on conversion and engagement metrics.”

“Backend-focused engineer with experience at Ramp, Easebuzz, and George Mason University, spanning data pipelines, workflow automation, and production reliability. Stands out for quantifiable performance gains, strong debugging instincts in distributed job systems, and translating ambiguous finance operations processes into measurable automation outcomes.”

Keeravani Chekuri

Screened

Mid-level AI/ML Engineer specializing in LLM systems and MLOps

Boston, MA3y exp

“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”

“Software candidate with hands-on experience using AI as a productivity multiplier across architecture, refactoring, testing, and code review. Has worked with LangChain-based multi-agent workflows to decompose complex engineering tasks for parallel execution, showing practical familiarity with emerging AI-native development patterns.”

“Engineer with hands-on experience modernizing healthcare platform authorization and EVV compliance workflows, including replacing hardcoded permissions with a Cerbos-based RBAC/ABAC system. Stands out for pragmatic AI-assisted development in regulated environments, with a strong emphasis on testing, auditability, and catching subtle business-rule failures before production.”

“Built GRETA, a full-stack multi-agent AI platform for SEO content analysis and blog-writing support, combining React/TypeScript, serverless GCP Cloud Run workflows, and LLM/tool orchestration at scale. The system reportedly reduced manual analysis by 60%, and the candidate shows strong hands-on experience shipping AI products in ambiguous environments and refining them through internal user feedback.”

“At ReferU.AI, designed and deployed an agentic RAG pipeline that automates multi-jurisdiction legal document drafting, emphasizing hallucination reduction through hybrid retrieval, validation agents, guardrails, and iterative regeneration. Experienced with orchestration frameworks (especially CrewAI) and rigorous testing/evaluation practices including human-in-the-loop review, adversarial testing, and production metrics/logging.”

Atharva Jagtap

Screened

Junior Full-Stack Software Engineer specializing in cloud and AI/ML applications

United States, Remote2y exp

“Full-stack engineer with hands-on experience across e-commerce personalization, enterprise RAG assistants, and cloud infrastructure automation. They’ve shipped AI features using Azure LLM APIs and vector search, improved recommendation engagement, and worked across frontend, backend, ML-informed analytics, and AWS infrastructure in early-stage environments.”

“Former startup co-founder/CTO who built a student-opportunity platform from scratch and engineered a real-time, Google-like search experience despite database limitations. Also brings deep modernization experience, including migrating legacy enterprise systems to containerized microservices and delivering major search-performance gains on 20+ year-old databases using CDC and OpenSearch.”

“Full-stack and AI systems engineer who has shipped both enterprise fintech and real-time voice AI products. At NCR, they repaired a broken transaction-fee workflow end to end using React/TypeScript, Lambda, and DynamoDB; at Lillup, they turned a research voice prototype into production infrastructure, cutting latency from 350ms to under 200ms and raising user ratings from 2.8 to 4.1. They also built an LLM side project that uses Claude and GPT to cross-review each other through structured JSON consensus rounds.”

“Built and deployed production NLP/LLM systems including a multilingual (5-language) health misinformation detection pipeline with latency optimization (batching/quantization/caching) and explainability (gradient-based attention visualizations). Experienced orchestrating end-to-end AI workflows with Airflow and Prefect, and partnering with customer support ops to deliver an AI agent for ticket summarization and priority classification with clear, measurable acceptance criteria.”

“Built and deployed “ApartmentFinder AI,” a multi-agent system using Google ADK, Gemini, and Google Maps MCP to automate apartment shortlisting and commute-time analysis, cutting a 45–70 minute user workflow down to ~30 seconds. Also has strong delivery/process chops from serving as an SDLC Release Coordinator, managing 52+ releases and reducing SDLC issues by 84%.”

“Backend Python engineer with end-to-end ownership of secure financial data systems integrating banking/credit/payment platforms, including automated ingestion and reconciliation of large financial statements. Built modular Dockerized Django REST services with pandas-driven validation/normalization and Postgres/Mongo persistence, and supported a phased migration from legacy VM services to AWS containers with stateless refactors and parallel-run integrity checks (run IDs/checksums). Works closely with platform teams on GitOps/CI readiness and deployment coordination (e.g., ArgoCD-managed sync policies).”

“Founding Robotics Research Engineer at Streamline Robotics building precision-agriculture automation: integrated FANUC + PLC harvesting with a Farm-ng Amiga (Jetson) platform using ROS2 Visual SLAM for GPS-free greenhouse navigation. Developed real-time YOLOv8 tomato detection/ripeness estimation for selective harvest and configured Cognex D900 3D inspection, plus redesigned FarmBot Genesis XL and built an automated imaging/labeling pipeline for growth tracking and adaptive watering.”

“At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.”

“Built and owned a production RAG search/Q&A platform at Data Integrity First for a client with a large, hard-to-search document library, deployed on AWS. Drove major adoption gains by adding source attribution (users trusted answers more) and improved system performance with guardrails, logging, and iterative chunking/OCR normalization—cutting fallback rate from ~22% to under 10%.”

“Robotics/AV-focused candidate who contributed to an F1TENTH autonomous vehicle college project, building key autonomy components from raw sensor data to driving commands. Strong in perception and state estimation (visual odometry, particle-filter localization), plus mapping (occupancy grids) and planning/control (RRT, Gap Follow, PID), with hands-on ROS tooling and simulation validation in Gazebo/RViz and ROS environment containerization using Docker.”

“JavaScript engineer with open-source experience on a database visualization library, focused on real-time rendering performance for large datasets (virtualized DOM rendering, requestAnimationFrame/debouncing, memoization) and on raising project quality via tests and CI performance benchmarks. Also built Kafka-based messaging documentation and sample producer/consumer apps to speed onboarding, and has experience diagnosing production issues including concurrency-related duplicate data problems.”

Krishna K

Screened

Junior Machine Learning Engineer specializing in multimodal systems and LLMs

Jersey City, NJ2y exp

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”

Lakshmi Meghana

Screened

Mid-level AI/ML Engineer specializing in production ML, MLOps, and NLP

Bristol, PA4y exp

“Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.”

Prithvi Jai Ramesh

Screened

Junior Robotics Engineer specializing in AI, perception, and autonomous navigation

Tempe, AZ1y exp

“Robotics software engineer with 2+ years of ROS/ROS2 experience who built a mobile robot stack from scratch (Fusion 360 → URDF → ROS) and integrated teleop, SLAM, and navigation. Worked in an ASU lab applying deep learning for person tracking on a TurtleBot setup, and solved real deployment issues like Raspberry Pi video-stream latency via compression and on-board processing. Also reports experience with CI/CD tooling (Jenkins) and Kubernetes.”