Vetted Jupyter Notebook Professionals

“Master’s in Business Analytics candidate with hands-on project experience spanning FMCG sales analytics, insurance risk modeling, and HR attrition analysis. Demonstrates strong SQL and Python fundamentals, including advanced CTE/window-function work, reproducible modeling workflows, and Power BI dashboards that translate analysis into clear business actions.”

“Software developer who uses AI pragmatically across the full stack to accelerate coding, testing, debugging, and documentation while maintaining strong human oversight. Stands out for treating AI output like any other code source—reviewing for architecture fit, security risks, performance, and standards before integration—and for coordinating multiple AI tools across backend, frontend, and test workflows.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

Rushir Bhavsar

Screened

Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training

1y exp

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

Arya Mane

Screened

Junior Full-Stack & AI/ML Engineer specializing in LLMs and multimodal document processing

Dallas, Texas1y exp

“Built a production RAG-based NBA player scouting assistant that embeds player profiles into FAISS, orchestrates retrieval and LLM recommendations with LangChain, and surfaces results via embedded Tableau dashboards. Demonstrates strong focus on evaluation/monitoring (batch tests, LLM-as-judge, latency/failure/token metrics) and has experience translating non-technical founder goals into DAPT + fine-tuning plans on curated data.”

Ankita A Khartmol

Screened

Junior Backend Software Engineer specializing in conversational AI and cloud APIs

Bangalore, India1y exp

“Backend/ML-focused software engineer who built and evolved a Python/FastAPI backend for a large-scale conversational AI platform, decoupling API and inference services to improve stability and deployment velocity. Experienced in production hardening (timeouts/fallbacks/monitoring), secure multi-tenant systems (JWT/RBAC/RLS), and low-risk migrations using shadow deployments and incremental traffic ramp-ups.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

“Built and deployed a GenAI-powered PhD career intelligence platform at NYU that maps academic backgrounds to career paths and converts long academic CVs into job-ready resumes. Stands out for treating LLM systems as structured production pipelines—combining NLP extraction, embeddings, orchestration, and AWS deployment—to improve recommendation quality and cut resume preparation time by 70%.”

“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”

“Analytics-focused candidate with hands-on experience across SQL data preparation, Python modeling, chatbot evaluation, and engagement metric design. They’ve worked on projects ranging from real estate deal analysis using 17,500+ Zillow listings to unemployment modeling, YETI chatbot performance analysis, and a generative-AI museum exhibit focused on participation and retention.”

“Built an internal RAG assistant for financial documents using FastAPI, OpenAI APIs, and vector search, improving document search speed and reducing manual effort for the business team. Stands out for a pragmatic approach to AI engineering: uses AI heavily for productivity, but keeps human judgment central and has designed retrieval, validation, and summarization workflows end-to-end.”

“Early-career candidate with a thoughtful, engineering-first approach to AI-assisted development: they use AI to accelerate implementation while retaining human ownership of architecture and final code quality. They recently built a speech-to-text workflow using Groq Whisper and showed practical judgment by designing around imperfect transcription accuracy with checks and fallback handling.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.”

“Solo-built and launched an AI logo generator SaaS in ~2 months using React/Next.js/TypeScript with managed auth and payments, deploying via Vercel/GitHub CI/CD. Also has hands-on AWS production experience running containerized services with Terraform-managed multi-environment infrastructure and strong reliability patterns for integrations/pipelines.”

“Software engineer with Citi Bank experience building real-time fraud validation/scoring for loan processing, spanning Spring Boot microservices and a FastAPI Python service secured with OAuth2/JWT. Delivered React/TypeScript operations dashboards and deployed containerized services via Docker/Kubernetes with Jenkins CI/CD, while also tuning databases (Oracle/Postgres) and handling high-volume latency/scaling issues using ELK, caching, and autoscaling on AWS.”

“Enterprise Principal/Lead CSM with experience owning high-profile tech, fintech, and government accounts end-to-end, including regulated AWS high-side deployments for a GPU-accelerated query engine. Built onboarding and VoC programs from the ground up, drove 90% adoption in 2 months, and influenced roadmap changes delivering 10x performance gains. Previously led Cloudera’s Apple relationship across 40+ teams and delivered 125% NRR through cloud/hybrid expansion and POCs.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”

Sampada shelke

Screened

Mid-level Machine Learning Engineer specializing in NLP, LLMs, and applied research

La Jolla, CA3y exp

“New grad SDE (AI/ML) who built and deployed an LLM-based chatbot framework used across technology, military, and banking contexts, focusing on model selection tradeoffs (latency vs accuracy) through prototyping and benchmarking. Also built a multi-agent "eaterybot" using PyAutoGen/AutoGen with a manager agent orchestrating specialized agents, and emphasizes rigorous testing with adversarial/edge-case datasets and hallucination checks.”

Sai Nekkanti

Screened

Mid-level Data Scientist / ML Engineer specializing in secure GenAI and financial compliance

Mount Laurel, NJ4y exp

“Built a production "sentinel insight engine" to tame information overload from millions of product reviews and support transcripts, combining Azure OpenAI (GPT-3.5) zero-shot classification with a fine-tuned T5 summarizer to generate weekly actionable product insights. Demonstrated strong MLOps/production engineering by adding drift monitoring with embedding-based detection, integrating REST with legacy SOAP/queue-based CRM via FastAPI middleware, and scaling reliably on Kubernetes with HPA.”

Jaskirat Singh

Screened

Mid-level Software Engineer specializing in cloud infrastructure and data platforms

Eden Prairie, MN5y exp

“Infrastructure/data platform engineer with hands-on GCP production experience, especially Bigtable, who led a migration from Azure Cosmos DB Cassandra API to Bigtable that removed throttling and cut costs by 50%+. Stands out for combining distributed data architecture, zero-downtime Kafka migration strategy, and Terraform/Python automation for deterministic multi-region GKE operations.”

Murali Marupudi

Screened

Mid-level Backend Engineer specializing in Python microservices and scalable systems

Jersey City, NJ4y exp

“Full-stack engineer with hands-on experience shipping both secure platform features and production AI systems. They combine React/TypeScript, Flask/Node.js, and PostgreSQL fundamentals with practical LLM and NLP implementation, including retrieval, schema-validated outputs, monitoring, and human-in-the-loop safeguards. Notable impact includes cutting manual review by 40% and reducing post-update error rates by over 20%.”