Vetted Matplotlib Professionals

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Built and shipped a real-time oncology risk prediction system used by doctors during patient visits, trained on clinical data in AWS SageMaker and deployed via FastAPI with sub-second responses. Emphasizes clinician-trust features (SHAP explainability, validation checks) and HIPAA-compliant controls (encryption, RBAC, audit logging), plus Kubernetes-based production operations with autoscaling, monitoring, and drift/retraining workflows; collaborated closely with oncologists at Flatiron Health.”

“ML/NLP practitioner with experience building an embedding-based ad matching and search system at Vericast (BERT embeddings + similarity search) to replace a third-party taxonomy approach, evaluated via a human-curated gold standard. Also built a custom NER pipeline at Allstate for auto accident claims calls using a bidirectional LSTM and achieved 90%+ F1, with a strong emphasis on production-grade ML workflows (testing, CI/CD, orchestration, versioning, validation).”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“AI engineer who built a production RAG-based internal analyst tool at BlackRock, fine-tuning an LLM on proprietary financial data and adding four layers of guardrails (input/retrieval/generation/output) to improve grounding and reduce hallucinations. Implemented a LangChain-based multi-agent orchestration (7 major agents) deployed on AWS ECS, with reliability measured via internal human evaluation, LLM-as-judge, and RLHF/drift monitoring.”

“Full-stack engineer who built an enterprise search platform (Codlens) delivering natural-language Q&A over Jira/Slack using embeddings, vector DB search, re-ranking (RRF), and LLM responses with source grounding. Also designed and benchmarked a distributed IAM system with Postgres transaction-log replication and Raft-based quorum consistency, reporting ~253 TPS at ~60ms latency in a multi-node setup. Experience spans early-stage startups (Zetic AI, Sagwara Capital) and large-scale orgs (Akamai, Atlassian).”

Anvith Reddy Dodda

Screened

Mid-level AI Engineer specializing in GenAI, NLP, and MLOps

Remote, USA3y exp

“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”

Jincheng Pang

Screened

Principal Data Scientist specializing in healthcare analytics and medical imaging AI

Sudbury, MA11y exp

“Developed an LLM-driven recommendation agent in Azure Databricks to triage oncology patients and trigger second-opinion case creation using medical claims and EHR data. Uses ICD-10/CPT/J-code features in prompts, embeddings + vector DB similarity, and a backtesting framework emphasizing recall to avoid missing clinically relevant cases while supporting business revenue.”

Dylan Tang

Screened

Intern Software Developer and ML Researcher specializing in medical imaging and computer vision

Chicago, IL0y exp

“AI/ML practitioner with experience spanning audio/LLM applications (built "Iota" using Whisper, tiktoken, and a local Ollama-served LLM) and healthcare ML (Facemed.ai; UChicago Radiology). Demonstrates a production-oriented mindset—focus on data/model fit, deterministic field testing, and operational safeguards—and has improved research evaluation workflows via a hash-table-based concurrent model tracking approach.”

Jainum Sanghavi

Screened

Mid-level DevOps Engineer specializing in cloud automation and Kubernetes platforms

Boston, MA2y exp

“Robotics/ML engineer who has built SO(3)-equivariant models for robotic manipulation, including custom equivariant layers and differentiable point-cloud rasterization/derasterization workflows. Also brings 2 years of DevOps experience in banking systems, automating CI/CD and infrastructure at scale (managed 180 OCI servers; reduced rebuild downtime by 80%).”

Niteesh Ganipisetty

Screened

Mid-level AI/ML Engineer specializing in Generative AI, NLP, and Computer Vision

Grand Rapids, MI4y exp

“Built an LLM-powered learning assistant (EduQuizPro/EduCrest Pro) that uses RAG over URLs and PDFs to generate quizzes, notes, and explanations for students/professors. Emphasizes production robustness—implemented dependency fallbacks (FAISS/Sentence Transformers/Gradio), CLI-safe mode, and NumPy-based indexing—along with a custom orchestration layer to keep multi-step AI workflows reliable.”

Swathi Sankaran

Screened

Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI

New York, NY10y exp

“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”

“Analytics-focused candidate with hands-on experience building SQL data pipelines and Python-based forecasting workflows for inventory and planning use cases. They emphasize data quality, stakeholder trust, and operational adoption, citing a 19% forecast accuracy improvement and strong experience translating analytics into dashboard-ready business metrics.”

Bhaskar Reddy

Screened

Junior Business & Data Analyst specializing in analytics and BI

Fairfield, CT2y exp

“Analytics-focused candidate with hands-on experience building SQL and Python workflows that turn messy multi-source data into reporting assets and dashboards. They show strong practical judgment around data quality, table grain, validation, and performance tuning, and they described an education-focused engagement project that reportedly improved course completion by 15% through targeted interventions and metric-driven stakeholder alignment.”

“Analytics professional with Deloitte experience building SQL and Python workflows for revenue, pipeline, and opportunity analytics at scale. They combine strong data engineering and modeling skills with business-facing delivery, citing impacts including 8-10% conversion improvement, ~$700K revenue protected, 12% YoY project acquisition growth, and 15% retention improvement in financial services.”

Saurabh Kumar

Screened

Intern Full-Stack Developer specializing in MERN and AI applications

Kanpur, India1y exp

“Frontend-focused engineer with hands-on experience building sophisticated browser UIs, including a real-time WebRTC video chat platform for IIT Kanpur students and a high-performance NBA analytics dashboard. Stands out for deep browser performance knowledge, expert-user interface design, and competition-level execution that helped earn a global rank 2 finish.”

“Backend engineer who built a Logistics-as-a-Service platform in Go, proactively refactoring a monolithic REST service into gRPC microservices to improve performance and maintainability. Led a 3-person team with disciplined code reviews, Dockerized DB migrations, and a canary-style rollout (5% traffic) monitored for latency and failures; also implemented JWT/OAuth2 RBAC and production-minded edge-case handling in an ordering system.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“Amazon engineer who built a production AI agent platform (Python/AWS Strands on Bedrock) that lets teams create tool-using, multi-agent workflows—e.g., agents that auto-triage and resolve customer support tickets by reading internal documentation and collaborating with a research agent. Previously worked in Deloitte on IAM using Ping Identity/Ping DaVinci orchestration, and applies orchestration thinking plus structured evaluation (LLM-as-judge, surveys, automated tests) to improve agent reliability.”