Vetted Anomaly Detection Professionals

Sharanya Rao

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare

Remote, USA3y exp

“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”

Jacob Allyn

Screened

Senior Physics & Gameplay Engineer specializing in Unity vehicle and multiplayer systems

9y exp

“Unity/C# engineer who led and shipped a backend-driven mobile AR platform (4D VIEWR) using Vuforia, enabling dynamic content delivery without app updates and focusing heavily on stability under real-world tracking/connectivity constraints. Also shipped authoritative-server multiplayer matchmaking/gameplay flow for NBA 2K vehicle modes, including server-side anti-cheat validation and live issue instrumentation. Recently built a self-hosted multi-agent LLM pipeline to accelerate game-dev workflows with strong reliability and cost/latency controls.”

varshini yaganti

Screened

Mid-level Data Analyst specializing in financial and customer analytics

Marietta, GA4y exp

“Analytics professional with experience at KPMG and Robosoft Technologies, working across financial and customer engagement data. They combine SQL, Python, experimentation, and BI dashboards to turn messy multi-source data into decision-ready insights, including a pricing test that improved conversion rates by 9%.”

Apoorv Bankey

Screened

Mid-level Backend Engineer specializing in distributed systems and FinTech

New York City, NY6y exp

“Engineer who uses AI and multi-agent workflows as a force multiplier while keeping architecture, security, scalability, and production quality under human control. Shared a concrete example of accelerating a backend-heavy SaaS email ingestion platform with authentication, role-based APIs, database models, and deployment setup using agent-style development and review.”

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.”

“Backend engineer focused on Node.js (Express/Fastify) and MongoDB who designed a multi-stage bill-approval workflow system for a manufacturing company, emphasizing RBAC, auditability, and scalability across multiple factory units. Also improved system robustness by catching a MongoDB connection leak in an Excise department project and has experience executing low-risk, incremental backend refactors with monitoring and rollback.”

“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.”

“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%.”

“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”

“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.”

“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 with Cigna experience building and operating an end-to-end AWS-based healthcare claims pipeline processing ~2TB/day, using Glue/Kafka/PySpark/SQL into Redshift. Strong focus on data quality and reliability (schema validation, monitoring/alerting, retries/checkpointing/backfills), reporting improved accuracy (~99%) and reduced latency, plus experience serving real-time Kafka/Spark data to downstream analytics with documented data contracts.”

“Built and launched a personalized sprint-planning dashboard to reduce recurring planning friction, choosing a simple, reliable scoring approach over a complex model. Iterated based on team feedback (more control, dependency clarity, performance), achieving a reported 20% drop in task spillovers; transparent about not yet shipping production LLM/RAG features but actively learning.”

Yukta Chikate

Screened

Mid-level Machine Learning Engineer specializing in safety-critical and uncertainty-aware ML systems

Brooklyn, NY5y exp

“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”

Muaaz Syed

Screened

Mid-level AI/ML Engineer specializing in NLP and conversational AI

Richardson, TX4y exp

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”

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.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

“Worked on industrial pump analytics at SitePro, where they built an anomaly detector using messy sensor and pump data and used historical failure and maintenance cost analysis to make the business case to stakeholders. They combine SQL/Python data preparation with practical stakeholder communication around metrics like churn and operational impact.”

“Analytics candidate currently at Facteus with hands-on experience turning messy transactional data into trusted reporting layers in Snowflake and Power BI. They combine SQL and Python automation with strong validation, performance tuning, and stakeholder-facing metric design, including cohort-based retention and segmentation work that improved trust and adoption of analytics.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

Adhiraj Chadha

Screened

Junior Software Engineer specializing in AI, data, and full-stack applications

Amherst, MA3y exp

“Builder with a mix of backend engineering, product instinct, and startup execution: they shipped a legal BI platform from scratch that handled 1,000+ cases, cut reporting time 80%, and saved $30K annually. They also move quickly in ambiguous environments, from launching a roommate app across iOS/Android after user discovery to building a RAG system with a 50+ case evaluation suite and a cloud dev environment in under 48 hours.”