Vetted PySpark Professionals

Ming Wang

Screened

Entry-level Software Engineer specializing in AI and FinTech

Hong Kong, Hong Kong1y exp

“Recent college graduate and software engineer who relies heavily on AI-assisted development, reporting that roughly 85% of code in a recent initiative was AI-generated and then manually reviewed. Has built customer-facing AI features including personalized recommendations and an internship chatbot tied to product advertising, with exposure to API communication, database checks, and conversation monitoring.”

“AI/ML engineer in financial services who has built both LLM-powered compliance tools and production fraud/credit risk systems at Ally Financial. Particularly strong in regulated, high-stakes environments: combines RAG/LLM architecture, rigorous evaluation, and human-in-the-loop governance, and also helped stand up a unified ML platform from scratch.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“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.”

“Built and deployed a production GenAI internal support agent at Bank of America (“Ask GPS/AskGPT”) using RAG on Azure, focused on reducing escalations and improving response quality for repetitive knowledge-based queries. Demonstrates strong production LLM engineering: custom LangChain orchestration, retrieval tuning to reduce hallucinations, rigorous offline/online evaluation, and model benchmarking with dynamic routing (e.g., GPT-4 vs Claude).”

“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 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.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

Ethan Lam

Screened

Junior Software Engineer specializing in data platforms and full-stack development

Toronto, Ontario3y exp

“Software engineer with Warner Music Group experience owning and shipping analyst-facing data products (marketing/streaming data dashboards) end-to-end with high adoption through continuous stakeholder feedback. Also builds side projects with TypeScript/React and domain-driven API design, emphasizing flexibility (including swapping databases mid-development) and pragmatic microservices reliability patterns (logging, timeouts, retry backoff).”

Pooja Dokuri

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG pipelines, and cloud MLOps

Remote, USA4y exp

“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”

John Hoffman

Screened

Senior Data Engineer specializing in Databricks, Spark, and AWS for government healthcare data systems

Windsor Mill, MD12y exp

“Python/AWS engineer focused on batch-processing and data workflows, including building reusable S3/boto3 utilities with reliability features and IAM-based auth. Has led low-risk legacy modernizations using parity testing plus a month of parallel production runs, and has owned production issues end-to-end (including fixing a client-side Excel macro) while contributing to significant AWS cost reductions (~$10k/month).”

Prasanna Chelliboyina

Screened

Mid-level Machine Learning Engineer specializing in forecasting, NLP, and GenAI

United States6y exp

“GenAI/ML engineer with production experience building multilingual LLM systems (English/Spanish) and RAG-based clinical documentation summarization at Walgreens, combining prompt engineering, structured output validation, and rigorous evaluation (ROUGE + pharmacist review). Also orchestrated end-to-end ML pipelines for demand forecasting using Apache Airflow, PySpark, and MLflow with scheduled retraining and production monitoring.”

Samatha Amsala

Screened

Mid-level Data Engineer specializing in cloud data warehousing and analytics

Omaha, NE6y exp

“Data engineer at American Express who owned end-to-end pipelines for transaction and customer data used in finance reporting and risk analytics, processing ~5–8M records/day. Built Airflow-orchestrated ingestion (including external APIs/web sources) with strong data quality controls, monitoring/alerts, and resilient backfill/retry patterns, and also shipped a versioned REST API serving aggregated metrics to analytics teams.”

“Built and shipped a production-grade agentic RAG system at CVS Health for patient adherence and medication recommendations, processing 20k+ patient records/day. Strong focus on real-world reliability: hybrid retrieval tuned with re-ranking (<400ms latency), strict JSON/schema validation and tool guardrails, and monitoring/drift detection that reduced MTTD from 6 days to 18 hours while improving recommendation accuracy (+8%) and cutting escalations (~23%).”

“Analytics professional with experience spanning HSBC and the University of Buffalo, combining banking risk/portfolio analytics with reproducible Python and SQL reporting workflows. Stands out for cleaning complex multi-source data, standardizing business metrics across dashboards, and delivering measurable impact including an 80% reduction in manual reporting and an estimated GBP 1.2M profit contribution from scorecard redevelopment.”

“ML/LLM systems engineer who has owned AI support automation products end-to-end, including ServiceNow-integrated incident routing, RAG-based resolution suggestion systems, and production stabilization. Stands out for combining hands-on platform work across PySpark, AWS Glue, FastAPI, Kubernetes, and Pinecone with measurable operational impact, including 30-35% MTTR reduction and 25-30% improvement in first-touch resolution.”

“AI/ML-focused engineer with practical experience building RAG-based and multi-agent systems, including architectures for retrieval, reasoning, context processing, and response generation. Stands out for combining LLM productivity gains with disciplined software engineering practices like validation, monitoring, and reproducibility.”

Anshika Bajpai

Screened

Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps

Bloomington, IN4y exp

“Engineer with impactful experience at Palo Alto Networks and Optum, focused on production automation and AI-powered internal tools. Built and owned an end-to-end RAG knowledge system adopted by 1000+ internal users with roughly 75% faster response times, and also transformed a legacy Optum coverage-feed workflow from 500+ minutes to under 3 minutes through data standardization and microservices refactoring.”

“Backend/data engineer with strong AWS production experience across containers (ECS) and serverless (API Gateway/Lambda/SQS), plus Glue-based ETL to Parquet for Athena/Redshift. Demonstrates hands-on reliability and security depth (Cognito OAuth2/JWT with JWKS rotation, idempotency/DLQs, monitoring) and measurable performance wins (Redis caching + query tuning), along with legacy-to-services modernization using parallel-run parity and feature-flagged cutovers.”

“Built and shipped a production LLM/RAG risk-case summarization and triage system used by fraud/compliance analysts, with strong grounding controls (evidence-cited outputs and refusal on low confidence). Demonstrates end-to-end ownership across retrieval quality, Airflow-orchestrated indexing pipelines, and compliance-grade privacy (PII redaction, RBAC, encrypted redacted logging, and auditable prompt/model versioning) plus a tight feedback loop with non-technical domain experts.”