Vetted Load Balancing Professionals

“DevOps/GCP-focused engineer with experience helping move complex/LLM-style prototypes toward production by clarifying requirements, hardening architecture, and driving OS-level test remediation to full pass rates. Debugs LLM/agentic workflow issues like distributed systems using GCP observability (Cloud Logging/Monitoring, GKE metrics) and implements alerting policies for proactive communication. Has delivered internal DevOps training from basics through end-to-end GCP infrastructure and deployment, improving engagement with real-world examples and analogies.”

“Software engineer (Yellow.ai) who built and productionized an AI-driven resume tailoring system using embeddings + Chroma RAG + QLoRA fine-tuning, deployed via Docker/Kubernetes with CI/CD on a CPU-only Oracle VM. Demonstrates strong reliability/evaluation rigor (custom hallucination/coverage/relevance metrics) and measurable business impact, including a 60% user satisfaction lift from improving chatbot intent accuracy with product and support teams.”

“Full-stack engineer in the healthcare domain (Humana) who owned an end-to-end member portal for benefits/claims/appointments, built with React and Spring Boot microservices on AWS. Notably migrated legacy batch data flows to a Kafka streaming pipeline and tuned consumers/partitions/backpressure to improve real-time consistency and achieve ~12% processing performance gains.”

“Master’s-program backend engineer with strong Java/Spring Boot industry experience who also owned a Python analytics service (Flask/Postgres, JWT, Celery/Redis) and optimized large-dataset performance via SQL/batching. Has hands-on Kubernetes microservices deployment and GitLab+Terraform CI/CD/GitOps workflows, plus experience supporting phased on-prem to AWS migrations and building Kafka-based real-time streaming pipelines.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

Paul Tran

Screened

Executive Technology Leader specializing in SaaS, federal consulting, and digital transformation

Tysons Corner, VA31y exp

“Former CEO of GravyWork with hands-on experience building an org-wide strategic plan and modernization roadmap for a growing company. Uses Michael Gerber’s innovation/orchestration/quantification framework and structured as-is/gap/to-be analysis to drive cost reduction, automation/outsourcing decisions, and leadership alignment through change.”

Mohana pavan Vankayala

Screened

Mid-level Java Full-Stack Developer specializing in microservices and AWS

Overland Park, KS4y exp

“Full-stack engineer (HCL Tech) with 4 years building enterprise, high-throughput microservices on AWS/Azure using Java/Spring Boot and React. Demonstrated measurable performance gains (40% throughput) through Redis caching, deep SQL/query tuning, and Kafka-based async refactors, plus strong DevOps/observability practices with Jenkins/CloudFormation and Datadog/Splunk.”

sai Pavan

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines

5y exp

“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”

Bilal Ahmed

Screened

Senior Linux Systems Engineer specializing in hybrid cloud and DevOps automation

7y exp

“Cloud/infrastructure engineer from ASM Research supporting federal healthcare systems, operating multi-cloud (AWS/Azure/GCP) environments at ~2000-server scale. Deep hands-on experience with Terraform/Ansible IaC, PR-based governance (Atlantis), and secure CI/CD (OIDC/least privilege), with concrete incident response wins and HA/failover testing improvements. Not an IBM Power/AIX specialist but comfortable translating virtualization/partitioning and ops practices to new platforms.”

“Engineer with hands-on experience embedding AI into software delivery workflows, including Claude-powered PR review, testing, debugging, and multi-agent coding pipelines. They pair AI automation with strong systems thinking around microservices, fault tolerance, multi-AZ design, caching, and security controls like WAF and rate limiting, and also experiment independently with RAG and multi-agent search projects.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Backend/data engineer who built a production-ready multi-agent financial intelligence system (Mycroft) that orchestrates specialized AI agents to analyze real-time market data using FastAPI and Pinecone vector search. Brings strong security/reliability instincts (rate limiting, JWT/OAuth2, retries/backoff, health checks) and has caught high-impact data integrity issues in financial migrations (timezone normalization across global legacy systems).”

“Cloud/hybrid infrastructure engineer with McKesson experience migrating tightly coupled healthcare applications to microservices on AWS EKS. Strong in IaC-driven standardization, CI/CD automation, and production observability (CloudWatch/Splunk/Prometheus/tracing), with demonstrated ability to debug complex incidents spanning Kubernetes and cloud networking.”

“Backend/platform engineer with Optum experience owning a production FHIR Member Access API aligned to CMS interoperability requirements. Built and scaled Spring Boot/HAPI FHIR microservices on AWS (Docker/Kubernetes) with zero-downtime CI/CD, and operated them with strong observability (Dynatrace, logs/metrics, alerting) and incident response. Also implemented a Kafka-based FHIR bulk data pipeline with schema versioning, idempotent processing, and reliable backfills/replays.”

“Built and shipped AI-powered workflow automation at Oracle, including an MCP-based agentic workflow with tool-calling and guardrails, plus Grafana monitoring and Confluence documentation. Also led a Django monolith-to-microservices migration at Chamsmobile using blue-green deployment and load balancer traffic splitting to avoid regressions while modernizing production systems.”

Aakanksha Desai

Screened

Junior Full-Stack Software Engineer specializing in React, Kubernetes, and AI-powered apps

Scottsdale, Arizona2y exp

“Backend/DevOps-leaning engineer managing multiple customer service platforms end-to-end (requirements through deployment). Built an in-house Python monitoring/alerting solution for Salesforce-to-Java contact sync jobs (Snowflake dependencies) that increased uptime ~60%, and helped modernize delivery by moving the team from manual releases to automated Jenkins-based deployments while coordinating an Oracle EBS→Fusion transition with business/data/IT stakeholders.”

Ramyasree K

Screened

Mid-level DevOps/Cloud Engineer specializing in AWS infrastructure automation

Boston, MA4y exp

“Frontend engineer with experience building a large-scale React + TypeScript administrative dashboard for an e-commerce platform, using Redux Toolkit plus TanStack Query to separate UI and server state. Emphasizes quality at scale through CI/CD automation, Jest/integration testing, and performance techniques like code splitting and caching, with experience coordinating integration across multiple teams.”

“Data/ML engineer with end-to-end ownership of donor-data deployments for a university foundation, delivering major performance and data-quality gains (500K+ records; 24h to 6h processing; duplicates 5% to 1%). Has put an LLM-assisted enrichment workflow into production with retrieval-grounded business rules, versioned outputs for traceability, and strong operational rigor around validation, logging, and CI/CD.”

Ruturaj Dixit

Screened

Junior Data Scientist specializing in AI/ML and product analytics

New York, NY2y exp

“Applied ML/data scientist who has owned backend-heavy AI systems end-to-end, including a market-signal platform on FastAPI/AWS and rapid MVP delivery in medical computer vision. Particularly interesting for teams needing someone who can combine model development, backend APIs, production debugging, and pragmatic low-latency architecture decisions.”

“Built a semantic search portal for a fellowship department and an AI-driven PR review pipeline, using AI selectively for boilerplate while retaining full ownership of architecture, security review, testing, and deployment. Has hands-on experience with multi-agent systems, monitoring, and security validation, with a notably disciplined approach to catching false positives and rewriting weak AI output.”

Dinesh Guguloth

Screened

Mid-level Software Engineer specializing in full-stack cloud-native applications

New York, NY4y exp

“Full-stack engineer with cloud and GenAI experience who has owned production features end-to-end, including a reporting dashboard optimized from 14s to 5s using query/API refactoring and monitored via AWS CloudWatch. Also productionized an OpenAI-powered chatbot using LangChain with prompt design, guardrails, and evaluation via production logs and user feedback, and has led incremental legacy-to-microservices modernization with parallel run to avoid regressions.”

“Full-stack engineer with experience spanning React/TypeScript frontends, Node.js backend services, and production systems on AWS. They’ve built real-time, event-driven inventory workflows using PostgreSQL, Redis, RabbitMQ, and Keycloak, and also drove a multi-agent LangGraph architecture for a self-healing systems project evaluated on simulated Kubernetes outages.”