Vetted Recommender Systems Professionals

Andrew Clayman

Screened

Senior Data Scientist specializing in ML, NLP, and production AI systems

Remote8y exp

“Machine learning/NLP engineer with deep Azure stack experience (Data Factory, Databricks/Spark, Delta Lake, Azure OpenAI, Azure AI Search) who built end-to-end production systems for semantic clustering, entity resolution, and hybrid search. Demonstrated measurable gains from embedding fine-tuning (~15% retrieval precision, ~10–12% nDCG@10) and designed scalable, quality-checked pipelines with MLOps best practices.”

Manali Patil

Screened

Senior Software Engineer & Engineering Manager specializing in cloud backend and manufacturing MES

Santa Clara, CA9y exp

“Customer-facing engineer who led recurring midnight ERP data-feed/B2B integrations from prototype to production, building reusable APIs and using Hangfire for job scheduling. Known for tight weekly customer iteration, strong documentation and test coverage (80%+), and cross-functional problem-solving with Operations/Quality/NPI to resolve data-collection and manufacturing-process constraints; has 2 customers live on the integration.”

“Senior product leader who helped transform Submittable from a single-program grant tool into a multi-program impact platform, driving ARR from $20M to $70M+ while improving retention and margins. Particularly strong in enterprise platform strategy and human-centered AI, with a clear philosophy of using AI to augment expert judgment rather than replace it.”

“Built and deployed an enterprise RAG application at Centene to help clinical teams retrieve insights from large internal policy document sets, cutting manual research by 30–40%. Implemented custom domain-adapted embeddings (SageMaker + BERT transfer learning) and hybrid retrieval (BM25 + Pinecone) to drive a 22% relevance lift, and ran the system in production on AWS EKS with CI/CD, MLflow, and Prometheus monitoring (99% uptime, ~40% latency reduction).”

“Data professional with ~4 years of experience, most recently at AIG (insurance), building ML/NLP systems for fraud detection and policy automation using transformers, CNNs, and clustering/anomaly detection. Also developed a RAG-based knowledge retrieval system, iterating across embedding models and moving to production based on precision and latency SLAs, then containerizing and deploying with SageMaker and CI/CD.”

PRAHARSHA JANDHYALA

Screened

Mid-level Data Scientist/Data Analyst specializing in ML, BI dashboards, and ETL pipelines

Dallas, TX4y exp

“Data/ML practitioner with experience at Humana and Hexaware, focused on turning messy, semi-structured datasets into production-ready pipelines. Built an age-prediction model from book ratings using heavy feature engineering and multiple regression models, and has hands-on entity resolution (deterministic + fuzzy matching) plus embeddings/vector DB approaches for linking and search relevance.”

“Built and owned production GenAI systems for both infrastructure automation and customer support. Most notably, they created a self-healing multi-cloud incident response system that automated 65% of tier-1 alerts and reduced application crashes by 75%, and also shipped a hybrid RAG support triage agent that automated 60% of tier-1 inquiries with human escalation guardrails.”

“Built a production AI stylist agent for retail store associates that pulls customer loyalty, address, and purchase-history data to generate in-store product recommendations. Demonstrates hands-on experience across agent orchestration, MCP-based tool integration, Vertex AI/GCP deployment, observability, resilience patterns, and pragmatic production tradeoffs like using PostgreSQL with pgvector instead of a standalone vector database.”

“Entrepreneurial product builder who created chaintrigger.com in response to early Web3 demand for real-time on-chain event reactions, offering an alternative to The Graph Protocol and achieving adoption among games and other Web3 projects. Currently developing new tools, dogfooding internally, and building distribution via personal network and a niche Twitter/X following to gather feedback and iterate quickly.”