Vetted Natural Language Processing Professionals

Harika M

Screened

Mid-level Full-Stack Developer specializing in FinTech and enterprise platforms

Plano, TX6y exp

“Engineer with a pragmatic, production-focused approach to AI-assisted development, using tools like Copilot and ChatGPT to accelerate coding while maintaining strict validation for correctness, security, and performance. Particularly notable for building a multi-agent incident-resolution workflow for a financial platform, with specialized agents for log analysis, root cause identification, fix suggestions, and test generation.”

“Full-stack engineer with hands-on experience spanning React/TypeScript frontends, Cloudflare serverless RAG systems, SQL-heavy backend redesigns, and computer vision workflows. He has shipped practical automation and reliability improvements with measurable impact, including cutting a video-validation reporting process from a week to 2 days and fixing a memory-heavy shipment system before Black Friday to support 30K+ orders successfully.”

“ML engineer at CGI who built demand forecasting models end-to-end, from feature engineering and training through AWS deployment. Stands out for a production-first mindset and strong skepticism of AI-generated code, including catching a Copilot-generated SQL query that would have caused a costly full table scan in production.”

KUSHWANTH MUTHYALA

Screened

Senior Full-Stack Engineer specializing in FinTech

Irvine, CA6y exp

“Engineer with hands-on experience building and deploying end-to-end ML inference pipelines using SageMaker, TensorFlow, Scikit-learn, and Kafka-backed real-time data systems. Brings a strong distributed-systems mindset and has already operated in a tech lead capacity through architecture decisions, code reviews, and cross-functional coordination. Especially compelling for teams building production AI/ML platforms that need both practical execution and sound engineering judgment.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

Bhagyashree Patil

Screened

Intern Applied AI Engineer specializing in LLM systems and data engineering

Los Angeles, CA1y exp

“Full-stack engineer with hands-on production experience across both traditional SaaS and LLM-powered support tooling. They owned a real-time ecommerce order tracking dashboard that improved support response times by 40%, and helped ship an AI support assistant using the OpenAI GPT API that cut ticket handling time by 30% through strong prompt design, retrieval grounding, validation, and human-in-the-loop safeguards.”

Pallavi Hogalagere Anandareddy

Screened

Mid Software Engineer specializing in backend systems and FinTech

Tampa, FL4y exp

“Backend/full-stack engineer with experience spanning banking implementations at FIS and hands-on product building in React, TypeScript, Node.js, SQL, and Python. Stands out for owning six bank go-lives end-to-end, building an AI resume evaluator, and proactively automating manual operations workflows to reduce errors and improve consistency.”

“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“GenAI/ML engineer with production experience at Cognizant and Ally Financial, building end-to-end LLM/RAG systems and ML pipelines. Delivered a domain chatbot trained from 90k tickets and 45k docs, improving intent accuracy (65%→83%), scaling to 800+ concurrent users with 99.2% uptime and sub-150ms latency, and driving +14% customer satisfaction. Strong in Azure ML + DevOps CI/CD, Dockerized deployments, and explainable/PII-safe modeling using SHAP/LIME to satisfy stakeholder trust and GDPR needs.”

“LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.”

“Software engineer with BitSight experience owning and revitalizing a critical internal Entity Management Portal (Django/React), clearing 30+ backlog items and boosting internal workflow efficiency ~40% through performance re-architecture (Redis caching) and disciplined testing. Also built a collaborative chore management platform (React/FastAPI) emphasizing responsiveness (optimistic UI) and scalability (connection pooling, Docker), and improved microservices security by centralizing secrets management with AWS Secrets Manager across multi-cloud environments.”

“Built a college AI chatbot during a master’s program, owning the full Python/Flask backend plus Google Gemini integration and a Postgres persistence layer (course info + conversation history), including caching/performance tuning. Also deployed and migrated ETL/ELT workloads from AWS Lambda into Kubernetes/EKS with GitHub Actions-based GitOps CI/CD, IRSA permissions, and Secrets Manager/S3/Postgres connectivity.”

“JavaScript/React performance-focused engineer who contributed upstream to an open-source virtualization/pagination library, fixing overlapping-fetch race conditions and introducing prefetch/deduping patterns that cut load times from ~3s to <900ms and reduced render thrash ~35%. Also built healthcare automation systems (clinical summary and claims triage), including a FastAPI + RAG pipeline that retrieved CPT/ICD evidence, improving decision accuracy from 67% to 86% and reducing turnaround time by 40%.”

“Backend engineer who built and optimized a high-traffic e-commerce platform in Python/Flask, focusing on scalability and reliability through service decomposition, Redis caching, and Celery-based background processing. Also integrated an AI intent-classification chatbot as a separately deployable inference service on AWS and has hands-on experience designing multi-tenant data isolation strategies in PostgreSQL.”

“ML/NLP engineer with experience at Charles Schwab building an NLP + graph (Neo4j) entity-resolution system to unify fragmented user/device/transaction data and improve downstream model quality and analyst querying. Has applied embeddings (SentenceTransformers + FAISS) with domain fine-tuning to boost hard-case matching recall by ~12% while maintaining precision, and has a track record of hardening scalable Python/Spark pipelines and productionizing fraud models via A/B tests and shadow-mode monitoring.”

“Full-stack engineer with early-stage startup experience who shipped and owned production Next.js (App Router + TypeScript) features end-to-end, including auth-aware APIs, caching, and post-launch monitoring/iteration. Demonstrates strong performance and reliability chops across React UX optimization, Postgres analytics modeling/query tuning (validated via query plans), and durable ingestion workflows with retries/idempotency.”

“Financial data engineer who has built and operated high-volume batch + streaming pipelines (200–300 GB/day; 5–10k events/sec) using AWS, Spark/Delta, Airflow, Kafka, and Snowflake, with strong emphasis on data quality and reliability. Demonstrated measurable impact via 99.9% SLA adherence, major reductions in bad records/nulls, MTTR improvements, and significant latency/runtime/query performance gains; also built a distributed web-scraping system processing 5–10M records/day with anti-bot and schema-drift defenses.”

“Data engineer/backend data services builder with end-to-end ownership of production pipelines for a Pfizer client, combining Python/SQL ingestion and transformation with strong data quality controls. Delivered measurable performance gains (~30% faster queries) and improved reliability through monitoring/alerting (Splunk, Prometheus/Grafana), structured logging, and incident response; also built internal REST APIs with versioning and caching and set up GitLab-based CI/CD with containerized deployments.”

“Full-stack React/Next.js engineer focused on routing and data-fetching reliability, including handling slow/unreliable networks with loading states, retries, and request cancellation to prevent stale data. Has delivered measurable frontend performance gains (reported ~40% improvement in time-to-interactive) using lazy loading, memoization, and profiling with React Profiler.”

Subhash Krishnamoorthy

Screened

Executive Technology Leader specializing in digital transformation, headless e-commerce, and cloud architecture

Chesterfield, VA25y exp

“Technology leader focused on business-aligned roadmaps and integration-heavy ecommerce platforms. Recently delivered an on-time launch for lutusooking.com (a premium Hamilton Beach brand) by coordinating UX/UI, component-based middleware, BigCommerce, Algolia search, personalization/recommendations, payments, and supply chain integrations, and later improved scalability via a Jitterbit iPaaS approach proven during Black Friday/Cyber Monday traffic.”

Rohan Varma Bandari

Screened

Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG

USA4y exp

“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”