Vetted Sentiment Analysis Professionals

“AI/full-stack engineer who has built both a real-time crypto sentiment platform from scratch and production enterprise RAG search systems at Kore.ai. Stands out for combining strong systems engineering with practical LLM evaluation, retrieval tuning, and careful human-in-the-loop design for high-risk network automation use cases with Cisco.”

“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.”

“Built and deployed an LLM-powered analytics assistant enabling business users to ask questions in plain English and receive validated Spark SQL executed in Databricks, with a Streamlit/Flask UI. Addressed strict client schema-privacy constraints by implementing a RAG strategy and ultimately leveraging AWS Bedrock and fine-tuned reference docs. Also has production ML pipeline experience using Docker + Airflow and AWS (S3/ECS/EC2) for financial classification models.”

“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.”

“Built and shipped a production LLM-powered healthcare support agent at UnitedHealthGroup, using LangChain + FAISS RAG on AWS SageMaker with CloudWatch monitoring and human-in-the-loop fallbacks for safety. Strong focus on reliability engineering (confidence gating, retries/timeouts, caching) and continuous evaluation loops; reported ~40% improvement in query resolution efficiency while reducing manual support workload.”

Dhyey Desai

Screened

Intern AI/ML Engineer specializing in RAG, multimodal AI, and LLM systems

Los Angeles, California0y exp

“Built and shipped 'PetPulse,' a production AI pet-health note system that records voice notes, transcribes them, converts transcripts into structured symptom/event data, and supports grounded Q&A over a user’s notes and vet PDFs. Demonstrates full-stack LLM product execution (FastAPI + GPT-4 + Firebase), with concrete reliability/performance work (async endpoints, caching, RAG/embeddings, function calling) and user-centered iteration with a non-technical product stakeholder.”

Sai Charan Kolla

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps on AWS

TX, USA5y exp

“LLM engineer who built a production document intelligence/RAG pipeline to extract structured data from thousands of unstructured PDFs, cutting manual review time by 60%. Experienced with LangChain and Airflow orchestration plus rigorous evaluation (labeled datasets, prompt testing, HITL review, monitoring) to improve accuracy and reduce hallucinations while partnering closely with non-technical operations stakeholders.”

Nirmal Attarde

Screened

Mid-Level Software Development Engineer specializing in backend microservices and cloud

3y exp

“Software engineer with Oracle experience deploying a BioCatch fraud-detection integration into HDFC Bank’s core banking platform, using phased rollout and real-time monitoring and reporting ~80% fraud reduction. Also built a modular speech-to-text product (VocalSense AI) achieving ~95% accuracy and has strong production incident response skills (15-minute recovery) plus AWS serverless API hardening for messy inputs.”

“ML/AI engineer focused on production NLP and voice AI systems in the restaurant tech space, with hands-on work spanning ASR, intent classification, LLM fine-tuning, and deployment monitoring at Presto AI. They highlight a 15% improvement in full-AI ordering rate and also built a restaurant sentiment analysis product at Wisely that they say became a standout feature in a $10M acquisition context.”

Alexander Awn

Screened

Director-level UX leader specializing in automotive and inclusive design

Farmington Hills, MI21y exp

“Design and UX leader in automotive and service design who helped ship a novel 2015 Ford Mustang app featuring early augmented reality/visual search capabilities. Combines hands-on product design with strong mixed-methods research and has influenced major business decisions, including research that reportedly saved a client $8M by stopping a poor market launch.”

“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.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“Built and deployed a production LLM/RAG chatbot at Wells Fargo for securely querying regulated financial and compliance documents, emphasizing low hallucination rates, explainability, and strict governance. Experienced with LangChain multi-agent orchestration plus Airflow/Prefect pipelines for ingestion, embeddings, evaluation, and retraining, and partnered closely with compliance/operations to drive adoption through demos and feedback-driven retrieval rules.”

“AI/ML engineer with production experience building LLM-powered document intelligence and customer support systems in healthcare/insurance, emphasizing high-accuracy RAG, long-document processing, and robust monitoring/fallback mechanisms. Also automates and scales ML lifecycle workflows using Apache Airflow and Kubeflow, and partners closely with non-technical operations stakeholders to drive adoption.”

“Built and deployed a production LLM/RAG knowledge assistant integrating internal docs, wikis, and ticket histories to reduce tribal-knowledge dependency and repetitive questions. Emphasizes reliability via grounding + a validation layer, and achieved major latency gains (>50%) through vector index optimization, caching, quantization, and selective re-validation. Comfortable orchestrating end-to-end LLM/data workflows with Airflow, Prefect, and Dagster, including monitoring and alerting.”

“Applied AI/ML Engineer at JPMorgan Chase who led a banker-facing LLM chatbot from an OpenAI-API POC to a production RAG workflow, including hallucination mitigation, automated evaluation in SageMaker, and operational monitoring with Dynatrace. Also delivers external technical education—hosted a hands-on Grace Hopper Celebration 2025 workshop teaching LangChain/LangGraph agentic workflows.”

“Data/ML practitioner with hands-on experience building NLP systems from prototype to production: delivered a Twitter sentiment classifier with robust preprocessing, SVM modeling, and Power BI reporting, and built entity-resolution pipelines for messy multi-source customer data (reporting ~95% improvement in unique entity identification). Also implemented semantic linking/search using SBERT embeddings with FAISS vector retrieval and domain fine-tuning (reported ~15% precision lift), and applies production workflow best practices (Airflow/Prefect, Docker, Azure ML/Databricks, Great Expectations).”

Nikhil Soni

Screened

Junior AI/ML Engineer specializing in LLM systems and retrieval-augmented generation

New York, NY2y exp

“Built and deployed a production LLM-powered market intelligence and decision-support platform for noisy, real-time financial data, using a high-throughput embedding + vector DB RAG architecture to reduce hallucinations while keeping latency and cost low. Operated it at scale with GPU-backed inference (continuous batching/quantization), FastAPI on Kubernetes, and Airflow-orchestrated ingestion/embedding/retraining workflows, with strong schema-based reliability and monitoring.”

Sri Harshitha Yannam

Screened

Junior Software Engineer specializing in AI/ML and cloud platforms

Austin, TX2y exp

“LLM/agent engineer who shipped a production "Memory Assistant" at HydroX AI, building a LangChain/LlamaIndex RAG memory pipeline on ChromaDB/FAISS with robust fallbacks (BERT/BART), prompt-injection mitigation, and 99.9% uptime monitoring. Also built a multi-step customer support agent using Rasa + OpenAI Assistants API with structured tool calling, guardrails, and human-in-the-loop escalation, and has experience hardening agents against messy ERP data via Pydantic validation, idempotency, and transactional outbox patterns.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“Built a production AI-powered insights platform for marketing teams analyzing large-scale social and news data, combining Java microservices, Kafka, Spark, React, and LLM-based retrieval workflows. Stands out for shipping customer-facing AI features with measurable gains in accuracy and latency, plus solid reliability practices for high-volume backend systems.”

Daivya Trehaan

Screened

Intern-level strategy and market research analyst specializing in FinTech and consumer insights

Dubai, UAE2y exp

“UCLA cognitive science student with hands-on startup strategy and market research experience, including work at AstroTech Builtin on user segmentation, fintech verticals, and expansion opportunities in remittance-heavy markets like the Philippines. She also builds her own consumer MVP concepts, combining campus-based user research, community-led growth ideas, and AI-assisted prototyping with a strong instinct for messaging and value communication.”

“Senior product and technology leader with 23+ years of experience driving modernization in complex enterprise finance and operations environments. He stands out for turning legacy, paper-based or fragmented systems into scalable digital products—cutting a warranty claims process from 30 days to near-instant and using AI to improve service efficiency and reduce testing effort by 30%+. Strong C-suite-facing operator who bridges strategy, architecture, UX, and organizational change.”