Vetted NumPy Professionals

“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”

“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”

“ML engineer/data scientist who deployed a production credit risk + insurance claims triage platform at Hartford Financial, combining XGBoost default prediction with BERT-based document classification. Demonstrated strong MLOps by cutting inference latency to sub-500ms and building drift monitoring plus automated retraining/deployment pipelines (MLflow, CloudWatch, GitHub Actions, SageMaker) with human-in-the-loop review and SHAP-based explainability for underwriting adoption.”

“Built and deployed NeuroDoc, a production-grade RAG system for PDF Q&A that delivers citation-backed answers with strong anti-hallucination guardrails. Experienced in orchestrating and scaling ML/LLM pipelines with Kubernetes, Airflow/Prefect, and PyTorch Distributed, and in building rigorous evaluation and citation-verification tooling to ensure reliability in production.”

“Full-stack engineer focused on application-layer product work (70–75%), with production experience building real-time operational dashboards (React/TypeScript + Node/Express + WebSockets + Postgres) and measurable impact (50% reduction in data entry time). Also owned a Flask backend for a SaaS product with token auth/RBAC, versioning, observability, and performance tuning, and has operated containerized apps on AWS (EKS, RDS/Aurora, S3, API Gateway) including handling a real latency/scaling incident end-to-end.”

Yashi Agarwal

Screened

Mid-level Machine Learning Engineer specializing in NLP, Generative AI, and RAG systems

Los Angeles, CA4y exp

“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”

SREEJA REDDY Konda

Screened

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

Kentwood, MI6y exp

“AI/ML Engineer at Fifth Third Bank who has shipped production fraud detection and risk analysis systems combining ML models with LLM-powered insights/explanations, including real-time monitoring, drift detection, and automated retraining under regulatory explainability constraints. Also built a hybrid-retrieval internal knowledge-base QA system (+20% top-5 relevance) and delivered a customer support chatbot that reduced first response time by 30% through strong stakeholder collaboration.”

Sowmya Kasu

Screened

Mid-Level Software Engineer specializing in Healthcare IT and cloud-native microservices

Northridge, CA6y exp

“Backend/ML engineer with healthcare experience at Kaiser Permanente building HIPAA-compliant Java/Spring Boot + GraphQL APIs integrated with Epic HealthConnect, including hands-on reliability/performance debugging using Prometheus/Grafana and resolver-level N+1 elimination. Also built an end-to-end malaria parasite detection ML feature (CNN/R-CNN) with evaluation, guardrails, and workflow integration, and has experience designing robust state-machine-based automation with retries, DLQs, and alerting.”

Bhavana Anna

Screened

Mid-level AI/ML Engineer specializing in fraud detection and Generative AI (RAG)

USA5y exp

“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”

sai Pavan

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines

5y exp

“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”

Teja Babu Mandaloju

Screened

Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms

Chicago, USA5y exp

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

Aniruddha Chakravarty

Screened

Junior Software Engineer specializing in cloud infrastructure, observability, and full-stack systems

Remote2y exp

“Built and productionized a predictive maintenance system (predictEngineLife) estimating Remaining Useful Life for PW4000 turbofan engines from large-scale, noisy telemetry—emphasizing modular pipeline design, deterministic preprocessing, and strong observability/guardrails. Also has hands-on experience diagnosing multi-agent LLM customer-support workflows (schema/state issues, fallback paths, regression tests) and has led developer workshops (GDG Pune) while partnering with sales teams on technical discovery and POCs.”

Nicholas Kim

Screened

Entry-Level Software Engineer specializing in full-stack and backend development

Houston, TX0y exp

“Full-stack developer who built a workout tracker feature end-to-end on the PERN stack (Postgres/Express/React), including relational schema design, REST APIs, and optimistic UI updates. Also has Next.js App Router experience (dynamic routes, SSG/React Server Components) and a strong quality mindset from Boston Scientific, where they used TDD to support clinically sound ECG analysis software and drove backend test coverage to 97%.”

Sai Bandaru

Screened

Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems

Boston, MA6y exp

“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”

Uttam Kumar

Screened

Intern AI Engineer specializing in LLM agents, RAG, and scalable cloud deployment

Atlanta, GA2y exp

“AI/LLM engineer at GPT integrators who built a production multi-agent enterprise workflow integration system, tackling hard problems in agent orchestration, layered memory, and custom RAG over enterprise/user data. Also built an education-focused agent solution integrating with Canvas, Zoom, and email to automate classroom admin tasks, and is currently applying agentic AI to insurance underwriting workflows in collaboration with underwriters.”

Sandipkumar Prajapati

Screened

Mid-Level Software Engineer specializing in full-stack systems and authentication

New York City, NY4y exp

“Full-stack engineer who led production modernization of a legacy, latency-sensitive application into a React + microservices platform, with heavy TypeScript backend work to improve reliability and maintainability. Has operated and scaled authentication/identity services in production, addressing peak-traffic latency spikes via database tuning and improved observability, and emphasizes idempotent, retry-safe workflow design.”

Rajeshwar Peri

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Chicago, IL5y exp

“Healthcare analytics candidate with hands-on experience turning messy claims and CRM data into validated reporting tables, automating monthly reporting in Python/Airflow, and operationalizing churn metrics in SQL and Tableau. They appear especially strong in stakeholder-aligned metric design and delivered a reported ~10% churn reduction through cohort analysis, segmentation, and at-risk member targeting.”

“Analytics engineer/data professional with strong healthcare and membership analytics experience, combining SQL, dbt, BigQuery, Python, and Tableau to turn messy source data into trusted executive reporting. Stands out for metric governance and stakeholder alignment work, including unifying conflicting business definitions and delivering a CMS market-risk model that identified $792M in excess payer costs.”

Clil Halevi

Screened

Junior Financial Markets Analyst specializing in quantitative research and FinTech

South Orange, NJ4y exp

“Analytics-focused candidate with internship experience at eToro and strong finance/product analytics exposure. They’ve worked on market sizing for Nordic stock launches, replicated a classic behavioral finance study using Python and CRSP data, and built cohort, retention, and churn analyses that informed onboarding and engagement recommendations.”

“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”

Rohan Chodapunedi

Screened

Entry-level Data Scientist specializing in LLMs and analytics

Folsom, CA1y exp

“Built a zero-to-one AI contract/policy QA agent for compliance and data teams, with a strong emphasis on trust, traceability, and clause-level citations rather than just fluent answers. They combine full-stack product ownership with practical LLM systems design, including hybrid retrieval, structured outputs, and evaluation pipelines to improve reliability, latency, and cost.”

“Engineer at Syniti who uses AI tools pragmatically to speed development while maintaining quality through rigorous validation, code reviews, and CI/CD. Most notably, they leveraged AI-assisted testing to increase test coverage from 10% to 70%, and they are actively exploring more advanced agent-based development workflows.”

“Engineer with hands-on experience embedding AI into software delivery workflows, including Claude-powered PR review, testing, debugging, and multi-agent coding pipelines. They pair AI automation with strong systems thinking around microservices, fault tolerance, multi-AZ design, caching, and security controls like WAF and rate limiting, and also experiment independently with RAG and multi-agent search projects.”

“Analytics professional with hands-on experience turning messy SAP enterprise data into trusted reporting layers and building end-to-end Python/Tableau analytics products. Stands out for combining technical rigor with business alignment—improving report accuracy by 30%, cutting refresh times by 25%, and independently delivering a CLV segmentation project across 96,000 customers that informed retention strategy.”