Vetted Model Monitoring Professionals

“Built an Arduino-based obstacle-avoiding robot using sonar/laser sensors and improved performance from 0.60 to 0.87 accuracy through sensor-fusion thresholding and iterative tuning. In an internship, optimized a legal-document NLP pipeline by switching to a distilled/quantized transformer and offloading inference to a GPU-backed Flask service, cutting inference time by 40%+ without added infrastructure spend.”

“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”

Sai somapalli

Screened

Senior LLM Engineer specializing in Generative AI, RAG, and multimodal assistants

USA6y exp

“GenAI/NLP engineer with experience building classification and summarization pipelines in PyTorch and deploying multimodal GPT-4-style workflows. Has integrated LLM applications across OpenAI, Azure OpenAI, and Amazon Bedrock, and uses LangChain/LlamaIndex/Semantic Kernel to orchestrate RAG and agent workflows with production-focused evaluation metrics like task success rate and groundedness.”

Ponugoti Sushma

Screened

Mid-level Machine Learning Engineer specializing in IoT, edge AI, and enterprise ML

Texas, USA5y exp

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

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.”

Piyush Rajendra

Screened

Mid-level AI/ML Engineer specializing in production RAG systems and MLOps

Athens, GA4y exp

“Built and deployed a GPT-4 + Pinecone RAG system that lets users query large internal document collections with grounded, cited answers. Demonstrates strong applied LLM engineering (chunking experiments, hallucination controls, metadata recency boosting) plus production-minded evaluation/monitoring and performance tuning (rate-limit mitigation via pooling/batching). Also effective at translating complex AI concepts to non-technical stakeholders through prototypes and live demos, helping secure client sponsorship.”

Varun Kothapalli

Screened

Mid-level AI/Machine Learning Engineer specializing in Generative AI, NLP, and MLOps

Saint Louis, MO6y exp

“Built a production LLM/RAG document analysis system for large financial documents (credit reports/PDFs) to help business analysts extract insights faster. Implemented end-to-end pipeline orchestration with LangChain, vector search (e.g., FAISS), and hallucination controls (context grounding, similarity thresholds, and no-answer fallback), delivered as a Dockerized Python API.”

Sravya Chunduri

Screened

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

Virginia, USA4y exp

“AI/ML Engineer with 3+ years of experience spanning RAG pipelines, MLOps, large-scale data workflow automation, and resilient Playwright-based UI automation. At Black Hawk Network and Wipro, they describe shipping production systems with strong observability and compliance controls, including reducing flaky automation failures from 30% to under 2% and automating 3+ TB/day reconciliation workflows.”

“AI/ML engineer with experience at Northern Trust and Persistent Systems building production LLM + RAG systems for regulated financial use cases, including liquidity forecasting, anomaly detection, and credit scoring. Emphasizes compliance-first design with explainability (SHAP), traceability (MLflow), and hallucination controls (FAISS + citation-grounded prompting), and has delivered drift-triggered retraining pipelines using Airflow and Kubernetes while translating model outputs into business-ready marketing segments.”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”

“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).”

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

“AI/ML engineer (Merkle) with hands-on experience deploying RAG-based LLM applications and real-time recommendation engines into production. Strong in cloud/on-prem architectures, GPU autoscaling, caching, and network optimization—delivered measurable latency reductions (40–70%) and improved retrieval relevance by systematically benchmarking chunking/embedding configurations and validating pipelines via CI/CD.”

“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.”

Prashanth Kedri

Screened

Mid-level Machine Learning Engineer specializing in MLOps, NLP, and predictive maintenance

AL, USA4y exp

“ML engineer with General Motors experience deploying production AI systems, including a BERT-based sentiment classifier for over a million customer support call transcripts (reported ~91% precision) and sub-200ms latency via FastAPI/Docker optimization. Also built predictive maintenance models and automated retraining/monitoring workflows using Airflow and MLflow, collaborating closely with non-technical customer support stakeholders.”

Shrinivas Bhusannavar

Screened

Mid-level AI Engineer specializing in agentic LLM systems and RAG platforms

San Jose, CA5y exp

“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”

“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.”

SaiSindhu Beeravolu

Screened

Mid AI/ML Engineer specializing in LLMs, RAG, and healthcare AI

Illinois, USA4y exp

“Healthcare ML/AI engineer with production experience at UnitedHealth Group, including an end-to-end readmission prediction system built on 50M+ patient records that improved accuracy by 18% and reduced preventable readmissions by 12%. Also shipped a clinically grounded LLM/RAG referral generator with human-in-the-loop safety controls, showing strong depth in regulated, high-stakes AI systems.”

Saketh Kota

Screened

Mid-level Data Scientist / ML Engineer specializing in Generative AI, RAG, and MLOps

Irving, TX4y exp

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”