Vetted Vertex AI Professionals

“ML/LLM engineer who has shipped production RAG systems (LangChain + HF Transformers + FAISS) with hybrid retrieval and cross-encoder re-ranking, deployed via FastAPI/Docker/Kubernetes and monitored with MLflow. Also partnered with wealth advisors at Edward Jones to deliver a client retention model with SHAP-driven explanations and a dashboard that improved trust, adoption, and reduced high-value client churn.”

“Full-stack engineer who built and owned a generative-AI pipeline end-to-end inside the Vibecut video editor using Next.js App Router/TypeScript, Gemini-based prompt routing, and Zustand state management, including concurrency-safe requests. Also integrated Python services to access newly released AI tooling, optimized Postgres/S3 data flows for thumbnails, and built Modal-to-Amplitude workflows for Reddit-driven sentiment/metrics in a pre-seed environment while also handling marketing.”

“Built and shipped a production LLM-powered internal operations automation platform using LangChain RAG (Pinecone) and FastAPI microservices, deployed on AWS EKS, serving 10k+ daily interactions. Implemented a rigorous evaluation/observability stack (golden datasets, prompt regression tests, MLflow, retrieval metrics, hallucination monitoring) that drove hallucinations below 2% and improved reliability, and partnered closely with non-technical ops leaders to cut manual lookup work by 60%+.”

Uday kumar swamy

Screened

Senior Machine Learning Engineer specializing in MLOps and NLP/GenAI

Chicago, USA9y exp

“Built a production LLM-agent framework for a startup that performs daily financial/trading analysis by combining live market data with internal tools, including a centralized memory module to prevent context drift and reduce hallucinations. Also implemented an Airflow-orchestrated retail price forecasting pipeline deployed to AWS endpoints, scaling parallel workloads via Kubernetes Executor and validating systems with rigorous functional + LLM-specific metrics and cross-team collaboration.”

“SRE-oriented infrastructure engineer who built an internal Vertex AI/Gemini knowledge chatbot to centralize product and development documentation, cutting routine support questions from 10+ daily to roughly 2. Also brings hands-on experience debugging Kubernetes production incidents and monitoring ETL/data-quality issues in Dataflow-based pipelines.”

“Built and productionized a multi-agent, LLM-powered document understanding system to replace manual review of long documents, using LangGraph orchestration plus RAG to reduce hallucinations. Implemented layered reliability controls (structured templates, checker agent, and human-in-the-loop feedback) and reported ~40% speed improvement after orchestration; also has hands-on Airflow experience for scheduled data pipelines.”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“At HPE, led and deployed an enterprise-grade LLM document intelligence platform for an insurance client, automating extraction from highly variable PDFs/scans/emails and raising field accuracy from 74% to 93%. Built a LangChain/Pinecone/OpenSearch RAG framework to cut hallucinations by 37% and operationalized LangSmith evals in CI, driving a 41% triage accuracy lift and >33% fewer incorrect resolutions while partnering closely with claims operations via HITL workflows.”

“Built and shipped LLM/RAG systems in finance and startup settings, including a Goldman Sachs document intelligence platform that indexed ~8TB of regulatory filings and delivered cited, conversational answers with <2s latency—cutting compliance research by ~4.5 hours per batch. Also developed LangChain-based agent workflows at Finta to automate CRM enrichment and investor lookup with strong testing, tracing (LangSmith), privacy guardrails, and auditability.”

“Data engineer at CenterPoint Energy who built and operated multiple production-grade GCP data systems: a daily Snowflake→BigQuery replication framework (150+ tables) with Monte Carlo/Atlan-driven observability and schema-drift protection, plus a FastAPI metrics service for pipeline health. Demonstrated measurable impact (40% faster dashboard queries, 70% less manual refresh work, zero data loss) and strong operational rigor (scaling Cloud Run jobs, SAP SLT reconciliation, quarantine patterns, CI/CD via GitHub Actions + Terraform).”

SASIREKHA GULIPALLI

Screened

Mid-level Data Analyst specializing in procurement, supply chain analytics, and applied machine learning

Alpharetta, GA4y exp

“Strategic sourcing professional specializing in seasonal apparel supply chains, combining Coupa/JD Edwards analytics with Excel/Python modeling and Power BI dashboards to drive cost reduction and OTIF gains. Notable for rapid mitigation of a 10-day factory delay affecting 12 holiday SKUs (preserved 95% of revenue) and for automating PO workflows to cut cycle time by 4.2 days and improve OTIF by 15%.”

“Data engineer with experience at Capital One and HSBC building and operating GCP-based data platforms. Led an end-to-end Oracle-to-BigQuery migration processing ~200–300GB/day using Dataflow/Beam, Airflow, Dataproc/PySpark, and Looker, achieving ~99.5% pipeline success and ~30% fewer data quality issues. Strong in production reliability, schema drift handling for external APIs, and BigQuery performance/serving patterns (materialized views, authorized views, versioned datasets).”

“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”

“Senior engineering executive with 8+ years leading large-scale SaaS modernization across AI, compliance, ecommerce, streaming, IoT, and travel. Has led a 150+ global engineering org, modernized seven cloud-native platforms for a $400M business, and consolidated travel systems processing $1B+ annually while staying hands-on in architecture, incident response, and AI integration.”

“AI/full-stack engineer in gaming analytics who joined Omnic.ai at a 2-person stage, helped grow with the company, and built both backend and frontend for real-time gameplay analysis products. He combines computer vision production experience with LLM/RAG systems work, and has already led 4 employees while shipping 12 models in a fast-moving startup environment.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

Sreelekha Vuppala

Screened

Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms

USA4y exp

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“AI Engineer with hands-on ownership of a production multi-agent RAG platform in financial services, spanning experimentation, architecture, deployment, monitoring, and iterative optimization. Stands out for measurable impact: 35% retrieval relevance improvement and nearly 50% reduction in manual operational analysis effort, plus strong experience making enterprise LLM systems safer and more reliable in production.”

“Backend/AI engineer who has owned production agentic systems end-to-end, including a CRM-integrated multi-agent financial workflow at Wow Payments that cut latency by 83% and achieved 98% uptime. Also built an AI real estate product ('Site IQ') by turning vague stakeholder goals into a geospatial autonomous agent using RAG, rapid prototyping, and tight validation layers around GPT-4 outputs.”

“Backend/data engineer who builds production FastAPI microservices and AWS serverless/Glue pipelines for SMS analytics and marketing segmentation. Led a legacy batch modernization into modular services (FastAPI + Glue/Athena + ClickHouse) using shadow-mode parity checks, feature flags, and incremental rollout. Demonstrated measurable performance wins (12s to sub-second SQL; ~40% CPU reduction) and strong incident ownership with proactive schema-drift prevention.”