Vetted Plotly Professionals

“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”

“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”

“AI/ML Engineer who built a production RAG-based LLM system for insurance policy documents, turning thousands of messy PDFs into a searchable index using LangChain, Azure AI Search vectors, hybrid retrieval, and FastAPI. Strong focus on evaluation (MRR/precision@k/recall@k, REGAS) and performance optimization (vLLM), with prior clinical NLP experience using BERT-based NER validated on ground-truth datasets.”

“Robotics engineer with hands-on experience scaling a multi-vendor heterogeneous warehouse robot fleet, building a distributed “traffic manager” for collision avoidance and real-time rerouting using CBS/MAPF and DCOP-style negotiation. Strong real-time/safety-critical systems background (RTOS, deterministic lock-free multithreading) plus modern perception and simulation tooling (CNN-LSTM/transformers, CARLA/Isaac Sim, VIO/GTSAM, camera-IMU calibration). Startup-oriented and comfortable moving quickly from prototype to production.”

“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”

“Data/QA-focused professional with experience at Lonza and Jio Platforms, owning end-to-end data reliability work for QC forecasting (SAP/LIMS) and driving root-cause/corrective actions for recurring environment configuration issues. Strong in SQL-based data standardization/validation, requirements tightening for API consistency, and coordinating multi-workstream delivery using Jira/Azure DevOps.”

Chandan Chalumuri

Screened

Mid-level Data Scientist specializing in ML, NLP, and Generative AI

Tempe, AZ4y exp

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

Vedang Jadhav

Screened

Mid-Level Software Engineer specializing in cloud-native microservices on AWS

New York City, NY5y exp

“Backend engineer with experience across healthcare and fintech platforms (Anthem, Citia) building high-throughput Python microservices with strong compliance/security focus (HIPAA, tenant isolation). Has integrated ML workflows into production systems (ResNet embedding-based image similarity) using async pipelines (Celery/Redis) and AWS (Lambda/S3/ECS), delivering measurable performance and fraud/content-integrity improvements at scale.”

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

Phani K

Screened

Mid-level AI/ML Engineer specializing in GenAI, NLP, and healthcare-financial ML

Terre Haute, IN4y exp

“ML/AI engineer with hands-on experience shipping healthcare AI systems, including an oncology risk prediction platform and RAG-based clinical decision support tools. Stands out for combining clinical domain context with strong production engineering across Spark, FastAPI, AWS SageMaker, monitoring, evaluation, and safety guardrails.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

“Full-stack and AI product engineer who has shipped both operational SaaS and LLM-powered research tools to production. Built a dispatch optimization system at Quickflora that cut manual effort by ~50%, and also developed grounded RAG and agentic systems using tools like LlamaIndex, Gemini, pgvector, and FastAPI with a strong emphasis on citations, reliability, and practical user workflows.”

“New grad software engineer with hands-on experience building production full-stack analytics infrastructure during a Swiftwise AI internship and independently shipping AI products. Stands out for combining strong TypeScript/React/backend fundamentals with practical RAG and agent-building experience, including a poker coaching assistant built solo from ingestion and retrieval through prompt tuning and evaluation.”

“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”

“GenAI engineer who has deployed production LLM/RAG chatbots for internal document search, focusing on reliability (hallucination reduction via prompt guardrails + retrieval filtering) and performance (latency improvements via caching). Experienced with LangChain/LangGraph orchestration for multi-step agent workflows and iterates using monitoring/logs and benchmark-driven evaluation while partnering closely with product and business teams.”

“GenAI/ML engineer with production experience at Cognizant and Ally Financial, building end-to-end LLM/RAG systems and ML pipelines. Delivered a domain chatbot trained from 90k tickets and 45k docs, improving intent accuracy (65%→83%), scaling to 800+ concurrent users with 99.2% uptime and sub-150ms latency, and driving +14% customer satisfaction. Strong in Azure ML + DevOps CI/CD, Dockerized deployments, and explainable/PII-safe modeling using SHAP/LIME to satisfy stakeholder trust and GDPR needs.”

Rohan Varma Bandari

Screened

Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG

USA4y exp

“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”

keerthana s

Screened

Mid-level Backend Software Engineer specializing in Python/FastAPI on AWS

Los Angeles, California4y exp

“Backend engineer with healthcare domain experience building AI-driven radiology workflow systems. Evolved tightly coupled APIs into secure, reliable FastAPI-based services by moving heavy imaging/data processing into idempotent asynchronous pipelines with retries, feature-flagged incremental rollout, and strong data-integrity controls (constraints, backfills, validation). Strong focus on defense-in-depth security for sensitive patient data (OAuth2/JWT, RBAC, and database-level protections).”

“Worked on industrial pump analytics at SitePro, where they built an anomaly detector using messy sensor and pump data and used historical failure and maintenance cost analysis to make the business case to stakeholders. They combine SQL/Python data preparation with practical stakeholder communication around metrics like churn and operational impact.”

Adhiraj Chadha

Screened

Junior Software Engineer specializing in AI, data, and full-stack applications

Amherst, MA3y exp

“Builder with a mix of backend engineering, product instinct, and startup execution: they shipped a legal BI platform from scratch that handled 1,000+ cases, cut reporting time 80%, and saved $30K annually. They also move quickly in ambiguous environments, from launching a roommate app across iOS/Android after user discovery to building a RAG system with a 50+ case evaluation suite and a cloud dev environment in under 48 hours.”