Vetted Data Quality Professionals

“ML/AI engineer with healthcare payer experience (Signal Healthcare, Cigna) who has shipped production fraud/claims prediction systems using Python/TensorFlow and exposed them via FastAPI/Flask microservices integrated with EHR and Salesforce. Emphasizes operational reliability and trust—Airflow-orchestrated pipelines with data quality gates plus SHAP-based interpretability, A/B testing, and drift/debug workflows—backed by reported outcomes of 22% lower false payouts and 17% higher model accuracy.”

“Built a production internal LLM/RAG assistant at CVS Health to cut time spent searching long policy and clinical guideline PDFs, combining fine-tuned BERT/GPT models with FAISS retrieval and a FastAPI service on AWS. Demonstrates strong real-world reliability work (document cleanup, hallucination controls, monitoring/drift tracking with MLflow) and close collaboration with non-technical clinical operations teams via demos and feedback-driven iteration.”

“Data engineer with Charter Communications experience modernizing large-scale AWS data lake pipelines: ingesting S3 data, validating against legacy systems, transforming with PySpark/Spark SQL, and serving via Iceberg/Delta tables. Worked at 50M–300M record scale, delivered >99.5% data match, and built monitoring/alerting (CloudWatch/SNS) plus retry orchestration (Step Functions) and data quality gates (Great Expectations).”

Ramcharan SreenivasaReddy

Screened

Mid-level Full-Stack Developer specializing in FinTech platforms and cloud-native microservices

Texas, USA6y exp

“Backend/platform-focused Python engineer who has owned FastAPI services with Postgres/SQLAlchemy and production-grade auth (JWT + RBAC). Experienced deploying and operating microservices on Kubernetes with GitOps (ArgoCD), HPA tuning, and Prometheus/Grafana monitoring, plus hands-on cloud-to-on-prem migrations and Kafka-based real-time streaming pipelines.”

Rupak Chand

Screened

Junior ML Data Associate specializing in AI training data and LLM prompt evaluation

Connecticut2y exp

“Applied ML/embodied AI practitioner who built an on-device gesture-control system for smart-home lights using Raspberry Pi + camera, focusing on privacy-preserving real-time inference and hardware-constrained optimization (async pipeline + TF Lite INT8). Also made a high-impact architecture decision for an ML content evaluation/QA pipeline processing millions of annotated text samples weekly, reducing batch runtime from ~6 hours to ~40 minutes while lowering compute cost.”

Abhishek Soni

Screened

Mid-level Full-Stack Developer specializing in React and scalable web applications

Mumbai, India3y exp

“Backend/data engineer with hands-on production experience across FastAPI microservices and AWS data platforms. Has delivered serverless and Glue/EMR-based ETL pipelines with strong observability (Prometheus/Grafana/Sentry, CloudWatch/SNS), schema-evolution resilience, and measurable SQL performance wins (5 min to <30 sec). Open to onsite meetings in the Bethesda, MD area and flexible on remote arrangements.”

Tejas Kolpek

Screened

Mid-level Solutions Architect/Engineer specializing in AI and data integrations

Mountain View, CA5y exp

“Solutions Engineer specializing in taking LLM copilots from demo to production, with a strong emphasis on enterprise security (RBAC/OAuth), grounded RAG behavior (cite-or-refuse), and operational readiness (eval loops, logging, runbooks). Experienced in real-time diagnosis of agentic/LLM workflow failures and in partnering with Sales/CS to run integration-first POCs that clear security and reliability concerns and accelerate rollout.”

Surya Vamshi Sriperambudooru

Screened

Mid-level AI Engineer specializing in healthcare claims analytics and RAG copilots

Remote, US4y exp

“Built a production "appeals co-pilot" for a healthcare claims appeals team, combining an XGBoost/logistic ranking model with a Python/LangChain RAG stack (FAISS + Mistral 7B) to surface high-probability appeal wins and speed policy-grounded drafting. Emphasizes reliability and trust: hybrid retrieval with metadata routing, citation/eval scripts, guardrails, and an explainability layer that non-technical stakeholders could understand and override.”

Prasanth Sai

Screened

Mid-level Data Engineer specializing in cloud lakehouse/warehouse pipelines

4y exp

“Data engineer with HCA Healthcare experience building and operating end-to-end AWS-based pipelines for clinical and operational reporting (50–100 GB/day), serving curated data into Redshift/Snowflake for Power BI/Tableau. Emphasizes production reliability (Airflow SLAs/retries/alerting, logging/observability) and strong data quality controls (reconciliations, schema/null/duplicate checks), and has shipped versioned REST APIs to expose warehouse data to downstream systems.”

“Accenture data engineer who greenfielded a supply-chain lakehouse platform, building an end-to-end medallion/Delta pipeline ingesting ~1.4TB/day from 17+ ERP/WMS/TMS/shipment sources. Delivered Gold datasets to Redshift/Synapse/Databricks SQL powering Power BI/Tableau with a 99.5% SLA, while cutting runtime 30% and cloud costs 16% through Spark/Delta optimizations and robust data quality controls.”

Lokesh Jain

Screened

Senior AI/ML Engineer specializing in supply chain and healthcare systems

Bentonville, AR6y exp

“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”

“Data and backend-focused engineer with hands-on experience spanning GenAI applications, production telemetry systems, and large-scale ETL pipelines. They combine modern AI stack work (React, FastAPI, LangChain, ChromaDB) with measurable production impact, including 90% lower DB insertion latency, 50% higher ETL throughput, and 99.9% data quality in distributed environments.”

Rocky Vienna

Screened

Executive Technology & Operations Leader (CIO/CTO/COO) specializing in PE value creation

10y exp

“Operations transformation leader with private-equity due diligence experience who rebuilt KIO Networks’ operating model using ITIL/COBIT/ISO 27001, stood up a PMO, and standardized tooling across countries—helping shift the business from co-location to managed services/cloud and improving EBITDA from -7% to +4% in 18 months. Also serves as a board member/mentor at EvoNexus and UCSD Rady, coaching founders on ICP, GTM, pricing, and investor readiness.”