Vetted HIPAA Professionals

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“GenAI Engineer at Infosys who built and deployed a production multi-agent RAG system for a top-tier bank, scaling to ~50,000 queries/day with 99.9% uptime. Drove measurable gains (45% accuracy improvement, 30% API cost reduction) through open-source LLM fine-tuning, Pinecone indexing/retrieval optimization, and AWS-based MLOps/monitoring, and has experience enabling adoption via developer workshops and customer-facing collaboration.”

“McKinsey data/ML practitioner who led production deployment of an entity resolution + semantic search platform for unstructured finance and healthcare data, integrating with legacy systems under HIPAA constraints. Deep hands-on stack across transformers (spaCy/HF BERT), embeddings + FAISS, and production MLOps/workflow tooling (Airflow, Docker, CI/CD, Prometheus/Grafana), with reported gains of +30% decision speed and +25% search relevance.”

“ML/AI engineer with production experience across retail and healthcare: built a real-time computer-vision shelf monitoring system at Walmart and optimized edge inference latency by ~30% using TensorRT/ONNX and pruning. Also partnered with CVS Health clinical/pharmacy teams to deliver a medication-adherence predictive model, using Streamlit explainability dashboards and achieving an 18% adherence improvement.”

“Healthcare GenAI engineer who built a HIPAA-compliant, auditable RAG-based claims decision support system at Molina Healthcare, processing 3M claims and delivering major impact (48% faster manual reviews, 43% higher decision accuracy). Deep hands-on experience with LangChain orchestration, vector search (ChromaDB/FAISS), embedding fine-tuning, and safety controls (confidence scoring, rule validation, human-in-the-loop escalation) for clinical workflows.”

Sayali Patil

Screened

Mid-level Python Full-Stack Developer specializing in Healthcare and FinTech

Everett, MA6y exp

“Backend engineer with hands-on experience building a fraud-transaction monitoring system in Python/Flask, architected as Dockerized microservices and integrated with Kafka for high-volume streaming. Demonstrates strong performance and reliability chops across PostgreSQL/SQLAlchemy tuning (EXPLAIN ANALYZE, N+1 fixes, bulk ops), multi-tenant data isolation, and scaling via background workers + Redis caching, plus real-time ML inference deployment using TensorFlow on AWS.”

Akshit Modi

Screened

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

Remote, USA5y exp

“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”

Atharva Bhide

Screened

Entry Software Engineer specializing in AI/ML and multimodal systems

Los Angeles, CA1y exp

“Built and shipped a production healthcare AI platform for a clinic in Brea, LA that combined LLM-based clinical report generation, voice agents for appointment workflows, and camera-based patient monitoring. Stands out for pairing multimodal AI architecture with production-grade reliability and compliance practices, while delivering concrete business results including 90% workflow automation, 200 hours saved per month, and a 60% improvement in customer retention.”

“Analytics candidate with hands-on experience at BCBS building HIPAA-compliant SQL/Snowflake/Tableau pipelines across fragmented legacy healthcare systems. Stands out for turning a 5-day claims reporting process into a near real-time 10-minute dashboard and for pairing strong data engineering discipline with reproducible Python-based churn modeling that drove measurable retention outcomes.”

“Analytics-focused candidate with strong SQL, Excel, Python, and Tableau skills who supports payroll-, compensation-, and finance-adjacent processes through rigorous data validation and reconciliation. Stands out for uncovering a duplicate-record mapping issue that exposed roughly $250K in revenue leakage and for building repeatable controls, dashboards, and automated checks to improve reporting accuracy.”

“AI/LLM engineer with strong healthcare domain depth who has shipped production-grade agents for care coordination and clinical workflow automation. Stands out for combining Knowledge Graph RAG, LangGraph orchestration, and rigorous eval/guardrail systems to improve reliability in high-stakes environments, with measurable gains in review time, hallucination reduction, latency, and clinician adoption.”

“Product-minded full-stack engineer with experience shipping engagement features and core communication systems at DribbleUp and Expys. Stands out for combining rapid MVP execution with rigorous iteration: delivered a leaderboard feature that lifted engagement by 8% initially and 20% overall, built a chat MVP in 3 days, and has hands-on experience deploying LangChain-based concierge agents with evals and human review.”

“UC Berkeley CS–trained hands-on engineering leader with executive experience spanning fundraising and board/customer communication. Led architecture and roadmap for AI-driven fintech platforms (including portfolio data, market signals, document processing, and Bitcoin trading), scaling global orgs (~100 people) and driving modular API-based designs that improved reliability, onboarding speed, and customer retention.”

“C-suite operator across multiple private-equity portfolio companies now raising capital for a thesis focused on meeting AI datacenter energy demand via AI-driven monitoring/inspection and condition-based maintenance for nuclear and natural gas generation. Has early traction with PE firms and is engaging target acquisition candidates, aiming to serve as an operating partner or CEO to execute the value-creation plan. Previously delivered a mobile-enabled workflow improvement for mutual fund valuation using HTML5 responsive design and CAB-driven agile development.”

“Customer Success Manager with enterprise experience managing the Walgreens contract at EarnIn end-to-end, coordinating executives/legal/HR/marketing and technical teams through benefits-platform integration, adoption, and renewal. Delivered 85% annual retention (despite store closures) and maintained a positive NPS, supported by usage monitoring, benchmarking, and monthly executive reporting. Also drove expansion at Unison by rolling out new insurance products to homeowners using savings/ROI-based positioning.”

“Built GoEngineer’s first production AI systems, including an end-to-end RAG pipeline for SolidWorks technical support using Azure Blob Storage, Azure AI Search, and Azure OpenAI, plus an AI summarization feature adopted by sales/customer success. Strong in productionizing LLM workflows with evaluation harnesses (golden sets, LLM-as-judge, red teaming, shadow deploys) and Azure infrastructure integrations (Redis, Service Bus, App Insights), and has also implemented a custom MCP server for agentic monitoring.”

Dinesh Kumar Patibandla

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for finance and healthcare

Texas, USA4y exp

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

“Built and shipped production LLM/RAG systems at Mayo Clinic, including a conversational AI assistant for patient pre-consultation and a clinical-trial matching tool for doctors. Implemented HIPAA-compliant de-identification and guardrails, plus real-time feedback logging and fine-tuning that improved response accuracy by 15% and reduced admin workload by 25%.”

“Infrastructure/operations engineer with deep IBM Power/AIX experience (AIX 7.x, VIOS, HMC/vHMC) managing ~15–25 LPARs across production and DR, including live DLPAR changes and structured performance troubleshooting. Also hands-on with PowerHA/HACMP incident recovery and failover testing, plus broader DevOps delivery building Jenkins CI/CD for containerized microservices and Terraform/Ansible IaC across AWS and Azure, and leading Solaris SPARC to x86 migration cutovers.”

“Data engineer at Capital One building AWS-based real-time and batch pipelines and backend data services for financial/fraud use cases. Has owned end-to-end pipelines processing millions of records/day, implemented dbt/Great Expectations quality gates, and tuned Redshift/Snowflake workloads (cutting query latency ~22–25% and reducing pipeline failures ~30–40%) while supporting 15+ downstream consumers.”

“Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.”

Akashreddy Madduri

Screened

Senior Backend Engineer specializing in real-time data platforms for FinTech and Healthcare

Plano, Texas6y exp

“Backend/data engineer with experience at JPMorgan building near real-time payment risk and fraud scoring pipelines using Python, Spark Structured Streaming, and Delta Lake, emphasizing auditability, security, and data correctness (dedupe/late events) to reduce false positives. Also led a legacy-to-cloud migration of claims/eligibility data at Cogna with parallel runs, phased rollout, and healthcare-specific validation (ICD-CPT mapping).”

Ravi Ada

Screened

Executive CTO specializing in digital health platforms, cloud & AI, and FHIR/HL7 interoperability

Carrollton, TX22y exp

“Healthcare diagnostics/health tech founder building Casandra.ai, an API-driven lab test catalog and ordering platform designed to standardize fragmented test catalogs and integrate into provider workflows via FHIR. Bootstrapped and built a deploy-ready product, drawing on prior startup experience and accelerator participation (Health Box, DreamIt Ventures).”

“Hands-on engineering leader who has scaled teams quickly (hired 20 engineers in 4 months) and led major architecture shifts including monolith-to-microservices and serverless, async AI-driven medical data ingestion/search. Also drove a versioned-inventory redesign with auditability and rollback that reduced operational errors by 22%, and demonstrates strong incident response with clear stakeholder communication.”