Vetted Neural Networks Professionals

“Built and deployed an LLM-powered mental health therapy assistant at AppHealth that segments users by stress level and delivers personalized, non-medical guidance. Implemented healthcare-focused safety guardrails (secondary LLM output filtering) and a multi-agent router workflow validated via statistical tests and therapist review, then scaled training/inference on AWS (EC2/Lambda/DynamoDB) with Kubernetes.”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”

“ML engineer/data scientist who deployed a production credit risk + insurance claims triage platform at Hartford Financial, combining XGBoost default prediction with BERT-based document classification. Demonstrated strong MLOps by cutting inference latency to sub-500ms and building drift monitoring plus automated retraining/deployment pipelines (MLflow, CloudWatch, GitHub Actions, SageMaker) with human-in-the-loop review and SHAP-based explainability for underwriting adoption.”

Yashi Agarwal

Screened

Mid-level Machine Learning Engineer specializing in NLP, Generative AI, and RAG systems

Los Angeles, CA4y exp

“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”

Teja Babu Mandaloju

Screened

Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms

Chicago, USA5y exp

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

Rahul Ganesan

Screened

Intern AI Engineer specializing in LLM systems, RAG, and cloud data pipelines

Washington, PA0y exp

“Built and deployed a production Dockerized multimodal (voice+text) LLM agent for knowledge management that retrieves from Notion and documents and falls back to Tavily-powered web search with citations when internal notes are missing. Emphasizes production reliability via model-switching fallbacks, caching, strict structured outputs (Pydantic/JSON schema), and MCP-based orchestration with state-aware gating and monitoring to reduce redundant tool calls and improve success rates.”

“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”

Chin-yu Wu

Screened

Junior Data Analyst specializing in sports analytics and business intelligence

Indianapolis, IN2y exp

“Analytics professional in the sports industry who has owned high-impact revenue and compliance data projects for the Colts, turning fragmented Ticketmaster and Salesforce data into trusted real-time reporting. Stands out for combining strong SQL/Snowflake engineering, rigorous validation practices, and stakeholder-facing metric design that drove a record 98% compliance rate and meaningful revenue recovery.”

“Intern-level full-stack engineer who has built across accessibility tech, ad-tech, healthcare software migration, and consumer analytics projects. Stands out for combining hands-on React/frontend work with backend integration, accessibility-aware routing logic, OAuth debugging, and pragmatic product decisions in highly ambiguous environments.”

Prabhav Karve

Screened

Junior Software Engineer specializing in data engineering and AI applications

Rochester, NY4y exp

“Data engineer/automation builder with experience at Rochester Regional Health and Accenture, focused on replacing fragile manual reporting with production-grade Azure, Python, and Snowflake pipelines. Stands out for combining strong systems thinking, rigorous validation, and practical AI/LLM usage to drive measurable outcomes, including a 34% throughput improvement and support for regulatory reporting that helped avoid €150M in penalties.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Robotics/embodied-AI practitioner who built a TurtleBot3 LiDAR-fingerprint localization pipeline end-to-end (autonomous data collection + multi-head NN) achieving ~30 cm error in a 10x10 m space. Also has industry experience at Infineon building large-scale production data/AI pipelines and rapidly fixing a deployed recommendation system by correcting upstream data normalization, improving accuracy by 20%+.”

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Outside sales engineer and full-cycle seller with hands-on outbound ownership across manufacturing (machine vision solutions) and real estate. Uses a highly structured, data-driven approach—ZoomInfo-based targeting, A/B-tested Salesforce cadences, and AI (Gemini 2.5) to automate personalized messaging at scale (~300 custom emails/week). Also has early-stage startup experience creating investor-facing marketing roadmaps and team operating rhythms.”

Sai Krishna Mallikanti

Screened

Mid-level AI & Data Scientist specializing in LLMs, RAG, and healthcare NLP

TN4y exp

“Built a production LLM/RAG solution for healthcare operations teams to query large policy and care-guideline repositories in natural language. Improved domain alignment using vector retrieval plus parameter-efficient fine-tuning and prompt optimization, validated through internal user testing and metrics, cutting manual lookup time by ~40%. Also has hands-on experience orchestrating automated ML pipelines with Apache Airflow.”

Srichandan Kota

Screened

Senior Full-Stack AI Engineer specializing in Generative AI and FinTech

Minneapolis, MN6y exp

“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”

Veena Vyshnavi Garre

Screened

Senior Full-Stack Software Engineer specializing in cloud-native systems and AI/ML

Hyderabad, India7y exp

“Backend engineer who significantly evolved an internal Resource Manager platform, moving from a monolith to microservices and improving onboarding speed while reducing integration errors. Has hands-on experience building reliable and secure Python/FastAPI APIs (Pydantic schemas, circuit breakers, caching, metrics/alerts) and leading zero-downtime migrations with strong data integrity patterns (dual writes, idempotency, reconciliation checks).”

“Founding product leader at Vergent who built out a 6-person product team and led a major modernization of a 16-year-old platform, collapsing 20+ legacy screens into configurable workflows and shipping a year ahead of schedule. Also shipped AI products in lending, including computer-vision-based document validation/parsing and ML for first-payment-default prediction, with a human-in-the-loop rollout strategy.”

“Built a production RAG-based healthcare chatbot to retrieve patient medical documents spread across multiple platforms, reducing manual and error-prone searching. Implemented semantic search with custom embeddings (Hugging Face) and Pinecone, deployed via FastAPI/Docker on AWS SageMaker with MLflow tracking, and optimized fine-tuning cost using LoRA while orchestrating retraining pipelines in Airflow.”

“LLM/MLOps engineer who has shipped a production RAG-based technical documentation assistant (FastAPI) cutting manual review by 45%, with deep hands-on retrieval optimization in Pinecone/LangChain (HNSW, hybrid + multi-query search, caching). Also brings healthcare domain experience—building Airflow-orchestrated EHR pipelines and delivering FDA-auditability-friendly predictive maintenance solutions using SHAP/LIME explainability surfaced in Power BI.”

“AI Engineer Co-Op at Northeastern University who built a production Patient Persona Chat Bot to help nursing students practice clinical interactions, fine-tuning Llama 3 and integrating a LangChain + Pinecone RAG pipeline deployed on Amazon Bedrock. Emphasizes clinical accuracy and reliability with guardrails, retrieval filtering, and continuous evaluation, and also brings strong data engineering/orchestration experience (Airflow, EMR/PySpark, ADF, dbt, Databricks, Snowflake).”