Vetted Load Balancing Professionals

“IBM Power/AIX infrastructure engineer who has owned a sizable production estate (50 Power servers / ~200 LPARs) spanning VIOS/HMC, SAN/NFS, and PowerHA clusters. Demonstrates strong incident leadership (LPAR outage + split-brain recovery) and a process-improvement mindset with measurable reductions in recurrence/MTTR, while also bringing modern DevOps/IaC experience (Jenkins, ArgoCD, Terraform, security scanning, canary/blue-green).”

Filmon Tesfay

Screened

Senior Full-Stack Developer specializing in cloud-native FinTech and AI platforms

New York, NY8y exp

“Full-stack engineer with strong production ownership: built and operated a real-time transaction monitoring/fraud-alerting system using Java Spring Boot, Kafka, Docker, and AWS with CI/CD. Demonstrates metrics-driven operations (latency, stability, consumer lag, true/false positives) and reliability patterns for integrations (idempotency, retries/backoff, DLQs, reconciliation/backfills), plus modern React/TypeScript + Node/Postgres architecture experience.”

Mo Arab

Screened

Executive Technology Leader specializing in Cloud, Managed Services & AI/LLM integration

Los Angeles, CA25y exp

“Engineering/technology leader with experience at Evocative and through a merger with Hivelocity, aligning tech roadmaps to managed services growth. Led multi-region self-hosted cloud and automation initiatives that cut delivery time from days to hours/minutes and informed cost-saving infrastructure decisions (reported $500K OPEX savings). Known for scaling teams with pod ownership, agile/intake governance, and disciplined rollout practices that protect uptime and security.”

Shital Agarwal

Screened

Senior Software Engineer specializing in backend, microservices, and full-stack web development

New Jersey, USA5y exp

“Software engineer who delivered a dynamic service-fee system for a Belgian online grocery e-commerce platform by carving out a Spring Boot microservice from a monolith, integrating Google distance APIs, Redis caching, and CI/CD for production rollout. Also built an OpenAI-powered university chatbot with agent/workflow orchestration during academia, emphasizing availability and fallback behavior.”

Butchi Venkatesh Adari

Screened

Mid-level Machine Learning Engineer specializing in LLM platforms and robotic perception

NewYork, NY4y exp

“Built and shipped a production multi-agent personal financial assistant at AlphevaAI on AWS ECS, combining FastAPI microservices, Redis/SQS orchestration, and Pinecone-based hybrid RAG (semantic + BM25) to ground financial guidance. Improved routing accuracy with an embedding-based SetFit + logistic regression intent classifier feeding an LLM router, and optimized UX with live streaming plus cost controls via model tiering and caching.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Built and shipped a production LLM-powered customer support agent for an EV charging platform using RAG plus internal APIs, automating session/payment issues and ticket routing. Emphasizes production readiness via guardrails, schema validation, state-machine orchestration, monitoring, and continuous evals, delivering a reported 35–40% reduction in support tickets and improved customer satisfaction.”

Ponugoti Sushma

Screened

Mid-level Machine Learning Engineer specializing in IoT, edge AI, and enterprise ML

Texas, USA5y exp

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

Haritha Peddi

Screened

Mid-level SOC Analyst specializing in SIEM detection, threat hunting, and incident response

USA5y exp

“Backend/AI engineer with production experience in payments/reporting systems and high-scale Node/NestJS services on AWS (ECS/ALB) using PostgreSQL, Redis, Kafka, Prisma, and Datadog. Shipped applied AI features including a Zendesk-embedded support copilot (summarization, draft replies, internal doc retrieval, playbook next steps) and an LLM-driven ops workflow agent with robust error taxonomy, retries/escalation rules, and auditability.”

“Full-stack product engineer with hands-on experience shipping React/TypeScript applications on AWS serverless infrastructure with Postgres. Stands out for combining measurable performance optimization (~30% faster APIs), UX improvements that lifted activation by 25%, and pragmatic platform thinking through reusable hooks and safe multi-tenant dashboard customization.”

“Backend/data engineer focused on production Python and AWS: built FastAPI REST services and a containerized ECS Fargate + Lambda architecture deployed via Terraform/CI-CD. Strong in data engineering (Glue/S3/Parquet/RDS) and operational reliability (CloudWatch/SNS, retries, schema-evolution handling), with experience modernizing legacy SAS reporting into Python microservices using feature flags and parity validation.”

“Full-stack/AI engineer who has shipped production systems spanning real-time analytics dashboards and an internal LLM-powered knowledge assistant. Experienced with RAG pipelines (embeddings/vector DB, semantic retrieval, query rewriting) plus evaluation loops and guardrails, and builds observable Kafka-based data pipelines monitored with Prometheus/Grafana.”

“Built and shipped a production LLM-driven data harmonization/record-matching pipeline for pharmaceutical datasets, combining normalization, embeddings/vector search, and an LLM validation step. Emphasizes production reliability via guardrails, confidence thresholds, idempotent/retryable stages, and human-in-the-loop fallbacks, with monitoring focused on manual review and error rates to reduce false positives.”

Sai Chinthalapudi

Screened

Mid-level Full-Stack .NET Developer specializing in cloud and microservices

United States4y exp

“Software engineer with healthcare platform experience at CVS Health, focused on APIs, SQL performance, and distributed systems. Worked on a 5-6 engineer team building a healthcare simulation platform and drove API/query tuning and caching improvements that cut response times by 50% for real-time, high-volume telemetry workflows.”

“Senior full-stack engineer with strong healthcare domain experience who has shipped an Azure OpenAI RAG-based patient medication support chatbot to production, driving ~10K queries/month and a reported 38% reduction in call center volume. Also builds polished real-time React/TypeScript pharmacy tooling and operates large-scale Python/Spark ETL pipelines (~12M records/day) with strong API design, observability, and cloud deployment experience across Azure/Kubernetes and AWS.”

“Hands-on engineer focused on productionizing LLM-powered assistants: builds RAG pipelines with guardrails, response schemas, and citation-grounded outputs, then hardens them with explicit NFRs (latency, uptime, security, cost). Experienced diagnosing agentic/LLM workflow issues in real time using observability and stepwise isolation, and supports go-to-market via developer demos, workshops, and pre-sales technical evaluations in microservices/Spring Boot environments.”

“Built and shipped an AI-driven interview evaluation pipeline at SeekOut that automated recruiter screening via a multi-stage LLM agent workflow (.NET backend, RabbitMQ orchestration, Python workers). Emphasizes production-grade reliability (idempotency, retries, strict JSON/schema validation), strong observability with OpenTelemetry, and measurable efficiency gains including ~40% reduction in token usage/cost.”

Rupini Polavarapu

Screened

Senior DevOps Engineer specializing in AWS cloud infrastructure and CI/CD automation

Atlanta, GA10y exp

“AWS platform/infra engineer with hands-on ownership of EKS cluster lifecycle (upgrades, node scaling, networking/ingress, and EBS-backed stateful storage) and reliability validation using Datadog plus CI/CD smoke tests. Also supported on-prem VMware environments and operated a hybrid on-prem-to-AWS setup over site-to-site VPN, including incident response and implementing change-controlled firewall processes and proactive connectivity health checks.”

Emmanuel Bakwowi

Screened

Senior DevSecOps Engineer specializing in multi-cloud Kubernetes and CI/CD automation

New York, NY10y exp

“Cloud/DevOps engineer operating across AWS and Azure, running Kubernetes workloads with secure CI/CD (GitHub Actions/Azure DevOps) and Terraform IaC. Has supported AIX/PowerHA systems in hybrid environments—handling failover testing, incident recovery, and performance troubleshooting (including multipath/storage-path issues)—and has led cutovers by managing dependencies, rollback, and stabilization.”

“Entry-level candidate who built NYC Lens, a real-time Gemini-based multi-agent system that processes live camera input, identifies landmarks, and returns structured contextual insights. Despite being a fresher, they show hands-on experience with deployment on Cloud Run, modular orchestration, noisy-data handling, and reliability patterns like retries, fallbacks, and explicit state management.”

“Built a production AI stylist agent for retail store associates that pulls customer loyalty, address, and purchase-history data to generate in-store product recommendations. Demonstrates hands-on experience across agent orchestration, MCP-based tool integration, Vertex AI/GCP deployment, observability, resilience patterns, and pragmatic production tradeoffs like using PostgreSQL with pgvector instead of a standalone vector database.”

Baliram Maurya

Screened

Senior Full-Stack Engineer specializing in Java microservices and Healthcare IT

USA11y exp

“Backend engineer with hands-on experience modernizing healthcare platforms in a startup-like team of 8-12 across engineering, QA, DevOps, and product. They personally drove scalable Java/Spring Boot microservices for healthcare workflows, including FHIR integrations, real-time data pipelines, and resilient integrations with legacy and third-party systems.”