Vetted TensorFlow Professionals

Vidhi Chhatbar

Screened

Mid-level Full-Stack Engineer specializing in cloud microservices and FinTech

Los Angeles, CA5y exp

“Software engineer with experience across enterprise (AIG, MSCI) and an early-stage startup (Job Map), owning production systems end-to-end. Built secure insurance microservices on Spring Boot with JWT/RBAC and AWS-based CI/CD/observability, plus Kafka streaming pipelines for financial data. Also shipped a GenAI personalization MVP using FastAPI and LLM APIs in a high-ambiguity startup environment.”

“Backend engineer who led the end-to-end modernization of FleetView into a scalable, event-driven system supporting 1,000+ users and 13,000+ assets, cutting API latency by ~40%. Also built an AI-powered exit interview analytics pipeline on Azure using GPT-4o with strong guardrails, validation, and evaluation practices, showing a rare mix of production backend rigor and applied LLM workflow experience.”

“Candidate has hands-on experience delivering production data and AI systems, including an AWS-based real-time data platform for a financial client at Deloitte and a production RAG workflow that cut manual search time by 40%. They stand out for combining strong data engineering depth with practical LLM governance, incident debugging, and stakeholder management across business and risk/compliance teams.”

Aarushi Mahajan

Screened

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

New York, USA4y exp

“Internship experience shipping production AI systems: built an end-to-end RAG platform (Python/FastAPI + LangChain/LangGraph + vector search) to answer support questions from unstructured internal docs, with a strong focus on hallucination prevention through confidence gating and rigorous offline/online evaluation. Also delivered an AI-driven personalization/analytics feature using an unsupervised clustering pipeline, iterating with PMs to align statistically strong clusters with actionable business segmentation.”

“Senior engineering executive who has spent the last decade leading large-scale platform modernization while staying deeply hands-on in architecture, debugging, and code reviews. He has led 120+ person organizations, built cloud-native SaaS and identity platforms across regulated and enterprise domains, and ties engineering decisions directly to revenue outcomes, including contributing to Wind River's $1B+ milestone.”

“Player-coach engineering leader currently running a Solution Architecture/FDE team responsible for both presales and postsales delivery. Stands out for combining enterprise systems thinking with hands-on AI product work: they built configurable tooling that sped delivery by ~30%, drove a Kafka-to-Pulsar architecture shift for scale, and spent the last two years building LLM-based document extraction and RAG inference pipelines shaped directly by user feedback.”

“Frontend engineer with JP Morgan Chase experience building data-heavy React/TypeScript products, including an AI-powered enterprise search application and workforce analytics dashboards. Stands out for combining reusable component architecture, Redux-driven state flow, responsive CSS, and production performance tuning for large-scale internal enterprise tools.”

“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”

“Worked on Sophia the humanoid robot, building production animation pipelines and enhancing human-robot interaction via perception and behavior orchestration. Experienced in stabilizing noisy perception-driven state transitions and designing smooth, user-centered behavioral flows, collaborating closely with artists, animators, and experience designers to translate creative intent into measurable system behavior.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“Built and deployed a production GenAI internal support agent at Bank of America (“Ask GPS/AskGPT”) using RAG on Azure, focused on reducing escalations and improving response quality for repetitive knowledge-based queries. Demonstrates strong production LLM engineering: custom LangChain orchestration, retrieval tuning to reduce hallucinations, rigorous offline/online evaluation, and model benchmarking with dynamic routing (e.g., GPT-4 vs Claude).”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“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.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“Amazon engineer who built a production AI agent platform (Python/AWS Strands on Bedrock) that lets teams create tool-using, multi-agent workflows—e.g., agents that auto-triage and resolve customer support tickets by reading internal documentation and collaborating with a research agent. Previously worked in Deloitte on IAM using Ping Identity/Ping DaVinci orchestration, and applies orchestration thinking plus structured evaluation (LLM-as-judge, surveys, automated tests) to improve agent reliability.”

“Early-career (fresher) candidate who built and deployed a production AI medical document chatbot using a RAG architecture (LangChain + Hugging Face LLM + Pinecone) with a Flask backend on AWS EC2 via Docker. Has experience troubleshooting real deployment constraints (model dependencies, disk space, container stability) and setting up continuous-style evaluation with fixed query test sets tracking relevance, latency, and error rate.”

“Built and productionized an autonomous research agent (AutoGPT) in a Docker/Kubernetes environment with Pinecone-based long-term memory and custom Python tools for analysis, visualization, and report drafting. Implemented layered guardrails (prompt templates, automated validation, self-critique loops, and monitoring) and achieved ~25% reduction in manual report generation time while scaling the workflow to support multiple concurrent users.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“Backend/platform engineer with payments domain experience, having owned core services for MasterCard’s global card tokenization and settlement platform. Built Django/Celery microservices plus Kafka/Redis real-time fraud streaming, delivering 27% latency improvement, sub-100ms fraud checks, and 18% fewer false positives. Strong DevOps/IaC background across Kubernetes, AWS ECS, Terraform, GitHub Actions, and GitOps practices for high-scale transaction systems (including UPI at PhonePe).”

“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.”