Vetted Apache Spark Professionals

Prakash Bhanu

Screened

Director of Software Engineering specializing in cloud, platform, and FinTech systems

Sunnyvale, CA22y exp

“Senior software engineering leader with broad 0-to-1 product experience spanning web apps, microservices, monoliths, messaging platforms, ML/AI products, and large-scale distributed systems. Notable examples include building a payroll/finance product for cast and crew, a distributed messaging platform, and a Walmart application deployed across multiple CDNs and clouds handling hundreds of TPS, with personal ownership across architecture, design, coding, and support.”

Srinivas Vasudevan

Screened

Junior Software Engineer specializing in distributed systems and FinTech

Durham, NC3y exp

“Built an end-to-end payment fraud monitoring dashboard with a React/TypeScript frontend, GraphQL backend, Redis hot path, and a production RAG chatbot, while solving real-time latency and scaling issues. Also shipped an OCR system on AWS EKS for a live manufacturing line at Troxler, improving production accuracy by 15% with custom preprocessing and model tuning.”

Sanjay Santhanam

Screened

Mid-level AI Software Engineer specializing in LLMs and FinTech data systems

San Jose, CA4y exp

“Backend/AI systems engineer focused on productionizing agentic document-processing workflows for large financial PDFs. They describe owning deployments end-to-end, combining Python, Redis, LLM function calling, RAG/ReAct-style orchestration, and strong reliability practices to deliver 80% faster processing, reduce parsing errors from 12% to ~1%, and sustain 99.9% uptime in high-concurrency environments.”

“Built and productionized an LLM-powered clinical assistant at a healthcare startup, re-architecting a prototype into a robust RAG system on AWS with guardrails, citations, monitoring, and automated tests for clinical reliability. Works closely with clinicians to convert workflow feedback into evaluation criteria and iterative system improvements, and has hands-on experience debugging agentic systems in real time (including during live client demos).”

“Built and deployed an on-prem, HIPAA-compliant LLM pipeline for oncology-focused clinical note generation and decision support, emphasizing grounded differential diagnosis and explainable reasoning via RAG to reduce hallucinations. Also created a LangGraph-based multi-agent academic paper search system integrating Tavily, arXiv, and Semantic Scholar with an orchestrator that routes tasks to specialized sub-agents.”

“AI engineer with production experience building a high-accuracy (98%) fraud detection system operating at real-time latency (1–2s) over millions of transactions, using a multi-model pipeline approach to meet performance constraints. Also implemented Airflow-orchestrated workflows (DAGs, retries, alerts) to replace brittle cron scripts and is currently pursuing a master’s project on real-time ASL-to-text conversion.”

“Data/analytics professional with experience across manufacturing and enterprise environments (Wisconsin School of Business project with CNH Industrial; roles/projects at Ascensia Technologies, S&C, and Adobe). Has hands-on work combining warranty/lifecycle tables with technician free-text notes using TF-IDF + tree models (XGBoost/Random Forest), and deep experience in entity resolution/reconciliation across mismatched financial systems using Python/SQL and fuzzy matching, with production-grade pipeline practices in Azure Data Factory/Databricks.”

“ML/NLP practitioner working across healthcare and business/finance use cases: currently fine-tuning a domain-specific Llama 3.1 model for safe reasoning over EHRs/clinical notes using RAG + RL/DPO and RAGAS-based evaluation. Has built UMLS-driven entity normalization pipelines with quantified quality gains and developed embedding/vector-DB systems (FAISS) for semantic matching and forecasting/recommendation applications at Aurora AI and Banxico.”

“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”

Felix Li

Screened

Intern Software Engineer specializing in data pipelines and full-stack web development

New York, NY1y exp

“Internship at Radar (geolocation infrastructure) where they owned automation of multiple geospatial data ingestion pipelines (including US/Canadian address ingestion), orchestrating Spark (Scala) jobs via Python-based Airflow and using GitOps-style CI/CD workflows.”

Vamshikrishna Bandi

Screened

Senior AI/ML Engineer specializing in Generative AI and agentic multi-agent systems

6y exp

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

Vasudha Prerepa

Screened

Mid-Level Java Full-Stack Developer specializing in cloud-native microservices

5y exp

“QA/validation-focused engineer with experience at Meta testing an ML+LLM content classification/summarization system, including production-vs-test behavior gaps. Built automated E2E validation and drift monitoring (PSI, KL divergence, embedding cosine similarity) run daily/multiple times per day and gated via CI. Also implemented Jenkins-orchestrated Selenium/API test suites in Docker at Capgemini and partnered with a business analyst to convert business rules into automated AI-driven validation checks.”

Praveen Nutulapati

Screened

Mid-level Generative AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

New York, NY6y exp

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

Kunal Singh Pundir

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and GenAI systems

USA, USA5y exp

“Built and owned an end-to-end AI-driven decisioning platform at Uber, combining LLM orchestration with typed tool contracts and a Snowflake-based RAG pipeline to make decisions fully auditable. Delivered large-scale measurable impact (120k requests/day, 18k cases auto-resolved/month) while improving ops SLA from 3 days to 6 hours and cutting incident response time nearly in half. Previously led a high-risk strangler-fig modernization of a legacy insurance platform across 120+ microsites at Accenture, coordinating across multiple squads with feature-flagged parallel cutovers.”

Sunil Parikh

Screened

Executive enterprise architect specializing in cloud, cybersecurity, and platform modernization

Plano, TX26y exp

“Architect with early startup experience (1999-2000) who later worked with Capital One evaluating startup products, strategy, and roadmaps. Brings a structured approach to innovation through market research, competitor analysis, risk assessment, gap analysis, and proof-of-concept thinking.”

“Built Baioniq, an enterprise LLM platform for automating extraction from massive unstructured documents like contracts and insurance claims. They demonstrate unusually strong production depth in agentic AI—scaling to 100k+ requests/day, processing 1M+ claim documents, and improving extraction accuracy through rigorous RAG architecture, evaluation, and fallback design.”

Sirisha Maddikunta

Screened

Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions

O Fallon, MO6y exp

“Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.”

“ML engineer with production experience at PayPal and Flipkart, owning high-scale systems across fraud detection, recommendations, and LLM tooling. Stands out for combining strong modeling judgment with practical platform engineering, delivering measurable impact like 22% fewer fraud false positives, 18% CTR lift, 40% less LLM manual review, and 30% faster redeployments.”

“Built and launched a production self-healing MLOps agent that autonomously diagnosed and fixed model training failures on Kubernetes GPU infrastructure. Combines deep AI infrastructure knowledge with full-stack product ownership, and has delivered measurable impact including 35% less infrastructure waste, nearly 50% less troubleshooting time, and 60% lower LLM API costs.”

“Architecture leader from Disney who managed system, AI, and data architects while staying hands-on in solution design. Has experience building LLM-based video advertising products, designing Kafka-based real-time data architectures, and using MVP/POC approaches to align product and executive stakeholders.”

Adam Sandler

Screened

Senior Full-Stack Engineer specializing in Python web platforms

7y exp

“Full-stack engineer with strong Python and React/TypeScript experience who has worked in lean startup environments on B2B SaaS hiring platforms. Most notably, they drove redesign work on developer search and matching systems at G2i, combining product collaboration, backend architecture, and database/query optimization to improve match quality and keep search responses around 100ms at scale.”

“Backend-heavy full-stack engineer with experience at Intuit (TurboTax Live) and Paytm payments, building and scaling Java/Spring Boot microservices for high-traffic transaction systems. Has hands-on wins improving peak-load performance using Redis/disk caching and Kafka event-driven patterns, plus React/Redux work for web app integration and strong monitoring practices with ELK.”