Vetted Elasticsearch Professionals

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

Apoorv Bankey

Screened

Mid-level Backend Engineer specializing in distributed systems and FinTech

New York City, NY6y exp

“Engineer who uses AI and multi-agent workflows as a force multiplier while keeping architecture, security, scalability, and production quality under human control. Shared a concrete example of accelerating a backend-heavy SaaS email ingestion platform with authentication, role-based APIs, database models, and deployment setup using agent-style development and review.”

“Automation-focused engineer with hands-on experience building production Python integrations, maintaining 60+ Jenkins pipelines across six product lines, and hardening CI systems in real-world environments. Most notably, they turned a tribal-knowledge Windows server maintenance process into a production workflow across nine servers, saving about 500 engineering hours annually.”

“Backend engineer who built and launched a warehouse locations/inventory microservice for Walmart, supporting a new product rollout with on-call war-room ownership and now running across all US distribution centers. Emphasizes reliability and correctness (background syncs, 2PC concepts, alerting) plus design-first API development in Python/FastAPI with OAuth/JWT and RBAC, and has led staged legacy-to-microservice migrations with continuous data integrity verification.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“GenAI/ML engineer with production experience at Cognizant and Ally Financial, building end-to-end LLM/RAG systems and ML pipelines. Delivered a domain chatbot trained from 90k tickets and 45k docs, improving intent accuracy (65%→83%), scaling to 800+ concurrent users with 99.2% uptime and sub-150ms latency, and driving +14% customer satisfaction. Strong in Azure ML + DevOps CI/CD, Dockerized deployments, and explainable/PII-safe modeling using SHAP/LIME to satisfy stakeholder trust and GDPR needs.”

“Software engineer with deep healthcare claims domain experience who has owned customer-facing portals end-to-end (Java/Spring Boot + React/TypeScript) and improved usability/performance based on real user feedback. Built microservices using REST and RabbitMQ with strong observability (Splunk/cloud metrics), and delivered an internal claims investigation dashboard that streamlined operations through centralized data, search, and filtering.”

“Software engineer with BitSight experience owning and revitalizing a critical internal Entity Management Portal (Django/React), clearing 30+ backlog items and boosting internal workflow efficiency ~40% through performance re-architecture (Redis caching) and disciplined testing. Also built a collaborative chore management platform (React/FastAPI) emphasizing responsiveness (optimistic UI) and scalability (connection pooling, Docker), and improved microservices security by centralizing secrets management with AWS Secrets Manager across multi-cloud environments.”

“JavaScript/Node.js engineer with open-source contribution experience (Mongoose) focused on connection pooling, test reliability, and memory/resource management. Has diagnosed and fixed real-world performance issues in an insurance claims application and improved resilience via failover DB design. Also experienced producing compliance/governance documentation for an EU-based biopharma, enabling stakeholders to make decisions quickly amid changing regulations.”

“Backend engineer with hands-on ownership of FastAPI/Django services using MongoDB and React integration, focused on production reliability and performance (Redis caching, Celery background jobs, automated testing). Has delivered AWS container deployments via GitHub Actions to ECR with scripted rollouts/health checks, and supported phased migrations with replication and rollback planning. Also built a real-time user-activity streaming pipeline addressing partition hot spots and consumer lag through partition-key strategy, idempotency, and monitoring.”

“Built an end-to-end automated trading system for Polymarket, including Go/Python execution services, Terraform-scheduled ETL/feature pipelines, and monitoring on modest hardware. Also shipped a production LLM+RAG signal verifier/explainer that grounds trade decisions in external context (news/social) with vector DB retrieval and guardrails, plus a lightweight RAGAS-style eval loop on ~50 resolved markets that improved signal faithfulness by ~15%.”

“Full-stack engineer who built an AI-driven document analysis and processing workflow end-to-end, including large-document ingestion, queued async processing, and low-latency retrieval for user-facing flows. Demonstrated practical performance tuning (moving heavy work off request path, polling, caching) and Postgres optimization validated with EXPLAIN ANALYZE, plus durable workflow resilience via retries and dead-letter queues.”

Sreeja Venkatakrishnan

Screened

Mid-level Full-Stack Developer specializing in Java/Spring and modern JavaScript frameworks

Virginia, USA4y exp

“Full-stack engineer with hands-on experience building real-time applications (Socket.io chat app) and data-heavy systems in banking/loan management. Comfortable across React and backend services (Spring Boot/Node), with a focus on scalable API design, database performance (indexing/pagination/caching), and deployment via CI/CD and cloud infrastructure.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Software engineer/product owner experience at GE Healthcare, owning a patient records and claims workflow product end-to-end. Built React/TypeScript + Spring Boot systems with contract-driven APIs (OpenAPI) and operated Spring Boot microservices using RabbitMQ, focusing on reliability patterns (idempotency, DLQs) and performance improvements driven by clinical feedback. Also created an internal monitoring/deployment dashboard that became the default tool for on-call and production support.”

“Built and productionized an internal LLM-powered search tool that helps engineers find the right SolidWorks macros using plain-English queries, using OpenAI embeddings and ChromaDB with strong logging/fallback safeguards. Experienced in diagnosing RAG/agentic workflow issues in real time and in hands-on API support, including fixing customer macros after SolidWorks version updates and driving adoption through reusable solutions and best practices.”

“JavaScript/Node.js engineer who contributes to open-source utilities focused on API integrations and JSON validation, including a 30–35% throughput improvement by profiling and optimizing deep-clone-heavy code paths. Strong in performance tooling (Node performance hooks, Chrome DevTools flame graphs), incremental/test-driven changes, and community-facing issue triage plus developer-friendly documentation.”

Mohithkumar Bolisetti

Screened

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

Strongsville, Ohio5y exp

“Full-stack engineer focused on modernizing legacy financial/compliance platforms into cloud-native, domain-driven microservices. Deep hands-on experience across Spring Boot/Kafka/Redis/Postgres-Mongo backends and React/Angular frontends, with strong CI/CD and Kubernetes/OpenShift deployment practices for real-time, high-volume workloads.”

“Analytics professional with hands-on experience unifying marketing-platform data through Fivetran and Snowflake, building reporting views, and catching source-to-report issues like timezone-driven spend discrepancies. They also owned subscription LTV/cohort analysis and engagement tracking initiatives, partnering with e-commerce, product, and senior leadership to turn behavioral and demographic data into dashboards, lead-qualification metrics, and lifecycle marketing insights.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

John Chance

Screened

Senior Machine Learning Engineer specializing in conversational AI and healthcare ML

Greenwood, LA9y exp

“ML/AI engineer with hands-on ownership of both classical recommender systems and safety-sensitive LLM agent platforms. They combine production MLOps depth with behavioral health domain experience, including clinical safety validation, explainability, and multi-agent orchestration, and cite measurable impact in both business metrics and latency reduction.”

“Engineering/product leader who builds business-aligned technology roadmaps and scales pod-based orgs with strong delivery discipline (OKRs, CI/CD, QA automation). Led a SaaS supply-chain application adopted by Fortune 100 customers, citing ~$4M MRR and ~87% gross profit, and has hands-on experience standardizing LLM + cloud/MLOps architectures with security/compliance guardrails. Also created the PISEK methodology and used it to run distributed innovation sprints (e.g., an AI ETA predictor moved from pilot to production).”