Vetted Plotly Professionals

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”

“ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).”

Bhavya Sri Gunnapaneni

Screened

Mid-level AI/ML Engineer specializing in fraud detection and NLP

United States4y exp

“Built production AI/RAG-style systems for message Q&A and insurance claims workflows, combining data ingestion, indexing/retrieval, and LLM integration with fallback modes. Has hands-on orchestration experience (Airflow, Prefect, LangChain) and cites large operational gains (claims processing reduced to ~45 seconds; manual review -50%; false alerts -30%) through automated, monitored pipelines and close collaboration with non-technical stakeholders.”

Dinesh Guguloth

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and GenAI

New York, NY4y exp

“Full-stack engineer with cloud and GenAI experience who has owned production features end-to-end, including a reporting dashboard optimized from 14s to 5s using query/API refactoring and monitored via AWS CloudWatch. Also productionized an OpenAI-powered chatbot using LangChain with prompt design, guardrails, and evaluation via production logs and user feedback, and has led incremental legacy-to-microservices modernization with parallel run to avoid regressions.”

Hari Billa

Screened

Mid-level Data Scientist specializing in machine learning, NLP, and healthcare AI

USA3y exp

“Senior data scientist with hands-on ownership of production ML and GenAI systems across enterprise churn, clinical Q&A, and real-time fraud detection. Stands out for combining strong MLOps discipline with measurable business impact, including $2M+ retained revenue, 10K TPS low-latency fraud infrastructure, and a clinician-reviewed RAG system that improved retrieval accuracy by ~38%.”

“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”

“ML/LLM engineer with production experience building a compliant RAG-based virtual assistant at Intuit, optimizing embeddings and FAISS retrieval (including PCA) for low-latency, privacy-controlled search and deploying via AWS SageMaker containers. Also built scalable Airflow+MLflow pipelines using Docker and KubernetesExecutor, cutting training cycles by 37%, and partnered with civil engineers/project managers at Aegis Infra to deliver predictive maintenance for construction equipment.”

“Backend/data engineer who builds Python (FastAPI) data-processing API services for internal analytics/reporting, emphasizing modular architecture, async performance tuning, and reliability patterns (health checks, retries, observability). Also migrated legacy on-prem ETL pipelines to Azure using ADF/Data Lake/Functions and implemented a near-real-time ingestion flow with Event Hubs plus watermarking to handle late events and deduplication.”

“Robotics software architect who built an automated pick-and-place palletizing prototype at BLACK-I-ROBOTICS, spanning perception (multi-RealSense fusion, segmentation, 6D pose, ICP), GPU-accelerated motion planning (MoveIt 2 + NVIDIA CuRobo), grasp generation, and safety (human detection + safe mode). Also brings cloud/CI/CD depth from VERIDIX AI (AWS Cognito/Lambda/ECS and CodePipeline stack) and demonstrated strong debugging chops by reducing outdoor rover EKF drift to ~5 cm via Allan variance-based IMU tuning.”

“AI/NLP-focused practitioner who built a zero-/few-shot LLM event extraction system on the long-tail Maven dataset, combining prompt-structured outputs with LoRA/QLoRA fine-tuning and rigorous F1 evaluation. Also implemented entity resolution/data cleaning pipelines and embedding-based semantic search using Sentence-BERT + FAISS, and has healthcare experience delivering a multilingual speech/translation mobile prototype using HIPAA-compliant Azure Cognitive Services.”

Santhoshi Priya Sunchu

Screened

Mid-level Data Scientist specializing in NLP and predictive modeling

Massachusetts, USA5y exp

“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“Built an AI-based voice interviewer platform at 7C Lingo to automate early-stage candidate screening, owning the full lifecycle from architecture through deployment and weekly production iterations. Implemented a TypeScript/Next.js recruiter dashboard with a Flask/Postgres backend and AWS S3, plus modular services for transcription/analytics/session management using state-driven async workflows. Also created an internal Whisper-powered transcription and editing tool that evolved into a collaborative, versioned, live-transcription system.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“ML/AI engineer who deployed a real-time, edge-based computer-vision pipeline for produce recognition in retail self-checkout to reduce shrink. Demonstrates strong end-to-end production chops: multi-camera data calibration/sync, ranking-based modeling for fine-grained classes, latency-focused optimization, and continuous A/B testing/monitoring with guardrails. Experienced with ML orchestration (Kubeflow Pipelines, Airflow) and CI/CD via GitHub Actions, and collaborates closely with store operations to make interventions usable in the checkout flow.”

Sreedivya Nagalli

Screened

Junior AI/ML Engineer specializing in deep learning and full-stack ML applications

2y exp

“Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.”

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.”

“Frontend engineer who co-built an AI-enabled marketing automation platform with multi-workspace tenancy, implementing database-scoped queries and RLS for isolation plus real-time UX (chat, voice transcription via Deepgram, autosave, Supabase Realtime). Emphasizes quality and speed through CI practices (linting/unit tests, planned Playwright) and has shipped fast iterations like Stripe prepaid card detection from overnight build through staged QA to production.”

“Built and deployed an enterprise RAG application at Centene to help clinical teams retrieve insights from large internal policy document sets, cutting manual research by 30–40%. Implemented custom domain-adapted embeddings (SageMaker + BERT transfer learning) and hybrid retrieval (BM25 + Pinecone) to drive a 22% relevance lift, and ran the system in production on AWS EKS with CI/CD, MLflow, and Prometheus monitoring (99% uptime, ~40% latency reduction).”

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

PRAHARSHA JANDHYALA

Screened

Mid-level Data Scientist/Data Analyst specializing in ML, BI dashboards, and ETL pipelines

Dallas, TX4y exp

“Data/ML practitioner with experience at Humana and Hexaware, focused on turning messy, semi-structured datasets into production-ready pipelines. Built an age-prediction model from book ratings using heavy feature engineering and multiple regression models, and has hands-on entity resolution (deterministic + fuzzy matching) plus embeddings/vector DB approaches for linking and search relevance.”

Erik Arriaga

Screened

Mid-level Data Engineer specializing in cloud data pipelines and machine learning

Austin, TX4y exp

“Experience spans college-built AWS-hosted Python/Flask web apps and enterprise data work at General Motors, including PostgreSQL query optimization on millions of records and multi-tenant-style data isolation using group-based, column-level permission grants. Also built an AWS-hosted meat price prediction dashboard using Dash/Plotly and ran large nightly data pipelines orchestrated with Apache Airflow.”

Almas Fathimah

Screened

Senior AI Engineer specializing in Generative AI and ML platforms

USA7y exp

“Built and owned a production RAG-based conversational AI system at Entera for real estate analysis, taking it from experimentation through AWS deployment, monitoring, and iterative improvement. Demonstrates strong practical judgment in retrieval design, LLM safety, and scalable Python service architecture, with measurable impact including 30-40% reduction in manual analysis time and roughly 30% better response accuracy.”