Vetted Terraform Professionals

“Full Stack Developer (recently at Cisco Systems) building end-to-end web applications with Angular frontends and Spring Boot microservices backed by MySQL/JPA, including JWT + role-based access. Has hands-on experience with high-volume, real-time data processing/visualization and has solved complex UI state consistency issues using RxJS BehaviorSubjects; also applies layered state patterns in React with Redux Toolkit and uses AI dev tools (Cursor/Claude) strategically.”

“Frontend/platform-leaning engineer who owned a shared React component library (design-system style) adopted across multiple SPAs, with strong discipline around SemVer, deprecation strategy, and developer documentation. Also has backend/distributed-systems experience diagnosing and fixing a Kafka microservice race condition and has led prioritization/migration planning in an unstructured monolith-to-microservices environment (Centene).”

“Full-stack engineer who owned end-to-end delivery of a customer-facing financial services web platform and built internal tooling for engineering teams. Strong in microservices and event-driven systems (Kafka/RabbitMQ), distributed transaction management (saga), and production performance/observability—achieving ~40% backend response-time improvement through database and query optimization.”

“Full-stack engineer with enterprise experience at Metasystems Inc. (and Qualcomm) building high-traffic, security-sensitive systems—owned a secure transaction processing module end-to-end using Java/Spring Boot, Python/Django, and React. Strong AWS production operations (EKS/ECS/Lambda/RDS/DynamoDB) with IaC (Terraform/CloudFormation), observability, and reliability patterns; also delivered resilient ETL/integration pipelines with idempotency/retries/backfills and achieved a 50% deployment-time reduction through CI/CD and modular refactoring.”

“Built and deployed an LLM-powered financial/regulatory document analysis platform at State Street, combining fine-tuned transformer models with a RAG pipeline over internal knowledge bases. Owned the productionization stack (FastAPI, Docker, SageMaker, Terraform, CI/CD) plus monitoring for drift/latency/hallucinations, delivering ~40% faster analyst review and improved reliability through chunking/embeddings and grounding.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“Cloud/Kubernetes-focused engineer with production ownership in multi-account AWS environments (GE) and EKS-based platforms (Lumeus.ai). Strong in incident response and reliability—diagnosed IAM-driven serverless failures (SQS/Lambda) and Kubernetes deployment issues (CrashLoopBackOff, memory pressure) with rollbacks, policy fixes, and improved monitoring. Built secure Jenkins CI/CD and delivered infrastructure via CloudFormation and Terraform for serverless and EKS stacks.”

“Infrastructure engineer with hands-on ownership of a scaled IBM Power/AIX estate (AIX 7.x, VIOS, HMC; 2 frames/20+ LPARs) supporting critical middleware and database workloads, including live DLPAR changes and VIOS/SAN outage recovery. Also brings modern DevOps/IaC experience building GitHub Actions pipelines for Docker/Kubernetes deployments and provisioning AWS environments with Terraform (EKS/RDS/VPC/IAM) using modular, review-driven workflows.”

“Cloud/DevOps-focused engineer with production experience in Linux, AWS, Kubernetes, and cloud-native architectures. Has built GitHub Actions CI/CD pipelines for containerized Kubernetes deployments and implemented Terraform-based AWS infrastructure with modular design and remote state/locking (S3 + DynamoDB) plus PR/CI-driven change control.”

“Full-stack engineer with experience at Capital One and Prime Softech owning production systems end-to-end: secure authentication (Java/Spring Security + React/Redux) through AWS ECS deployments with Terraform and CI/CD. Strong reliability/observability focus (Prometheus/Grafana/ELK/CloudWatch) with quantified improvements (15% reliability gain, 30% fewer post-release defects). Also led legacy monolith-to-microservices refactors and built real-time Kafka/Spark ingestion pipelines for analytics/fraud detection.”

“Data engineer (~4 years) with full-stack delivery experience (Next.js App Router/TypeScript + React) building a real-time operations monitoring dashboard backed by Kafka and orchestrated data pipelines. Strong production focus: Airflow + CloudWatch monitoring, automated Python/SQL validation (99.5% accuracy), and CI/CD with Jenkins/Docker; has delivered measurable improvements in latency, pipeline reliability, and query performance (Postgres/Redshift).”

“LLM-focused engineer who has shipped multiple production-grade AI reliability systems: an LLM output validation/monitoring service (FastAPI) with prompt versioning and failure analytics, plus a RAG feature using embeddings/vector DBs with retrieval thresholds, schema/context validation, and safe fallbacks. Strong in evaluation loops (groundedness, schema accuracy, human review) and scalable pipelines for messy document ingestion with observability and early detection of data quality issues.”

“Full-stack engineer currently at Southern Glazer’s who built and owned a real-time commercial finance expense analytics dashboard end-to-end (Next.js App Router + TypeScript), including post-launch monitoring, data quality checks, and stakeholder-driven iteration. Strong data/analytics backend experience (Postgres modeling and Databricks Delta Lake pipelines) with demonstrated performance wins—e.g., cutting a key reconciliation query from 8–12s to <400ms and improving frontend load time ~40% with a 25% bounce-rate drop at Verizon.”

“Research Assistant at the University of Houston who built and live-deployed a production RAG system for 1000+ research documents, using hybrid retrieval (dense+BM25+RRF) with cross-encoder reranking and RAGAS-based evaluation; reported 66% MRR, 0.85+ faithfulness, and 68% lower LLM inference costs. Also built a deployed LangGraph multi-agent research system (Researcher/Critic/Writer) with tool integrations (Tavily, arXiv) and dual memory (ChromaDB + Neo4j), plus freelance automation work delivering a WhatsApp chatbot and n8n workflows for a wholesale clothing business.”

“Data engineer focused on sales/marketing analytics pipelines, owning ingestion from CRMs/ad platforms through warehouse serving and dashboards at ~hundreds of thousands of records/day. Built reliability-focused systems including dbt/SQL/Python data quality gates with alerting, a resilient web-scraping pipeline (retries/backoff, anti-bot tactics, schema-change detection, backfills), and a versioned internal REST API with caching and strong developer usability.”

“Accenture consultant who shipped an LLM-based production solution during a client cloud migration to parse application code and identify only the database objects actually used, cutting migration time by 30% and accelerating realization of cloud cost benefits. Emphasizes production robustness with timeouts/retries/fallback routing, validation, observability, and a disciplined eval/monitoring loop that turns failures into regression tests.”

Bhakti Patel

Screened

Senior Full-Stack Software Engineer specializing in .NET, Python, and cloud-native systems

Worcester, MA11y exp

“Full-stack engineer who owned an end-to-end production feature for a Piraeus Bank stock exchange module, spanning React/TypeScript, backend services, and cloud operations with Docker + CI/CD, delivering reported 90% faster API responses and improved uptime. Also built a Smartwound research MVP on AWS, creating a Python image-processing/scoring pipeline to ship despite unclear image-analysis specs.”

Sharanya Teegala

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring and AWS microservices

Minneapolis, MN5y exp

“Full-stack engineer with experience at Wells Fargo and Salesforce building regulated, customer-facing financial systems and internal DevOps tooling. Deep in microservices and event-driven architectures (Spring Boot, Kafka/RabbitMQ) with strong CI/CD automation, contract testing, and observability; delivered measurable impact including 60% faster deployments and 40% fewer support tickets.”

Jorge Mendoza

Screened

Senior Full-Stack Developer specializing in Python, AWS serverless, and data workflows

Remote11y exp

“Backend/data engineer from ALDI Tech Hub who modernized legacy analytics (Excel/SAS) into production-grade Python services on AWS serverless (FastAPI on Lambda behind API Gateway with Step Functions). Strong in reliability and operations (Cognito auth, retries/timeouts, structured logging, CloudWatch alarms) and data pipelines (Glue ETL with schema evolution); delivered measurable SQL tuning gains (30s to 2s, 70% CPU reduction).”

Sanjana Kirthi

Screened

Junior Software Engineer specializing in backend platforms and cloud-native systems

USA3y exp

“Backend engineer from Emphasis who modernized legacy, tightly coupled workflow systems into observable, event-driven microservices using Kafka. Led a monolith-to-microservices refactor with shadow traffic, feature flags, canary rollout, dual writes, and reconciliation, and strengthened reliability with idempotent consumers, DLQ/replay, and an outbox pattern to prevent DB/event inconsistency. Strong focus on secure multi-tenant APIs (OIDC/JWT, RBAC/ABAC, Supabase-style RLS) and frontend enablement via OpenAPI and typed client generation.”

Shashank Venkataramana

Screened

Mid-level Software Engineer specializing in AWS cloud infrastructure and data platforms

Scottsdale, AZ4y exp

“Backend/infra-focused software engineer who built an autonomous Python API-orchestration agent using asyncio with strong reliability and observability (trace IDs, structured logs, retries/timeouts) and containerized dev workflow. Experienced deploying Python services to Kubernetes with Helm and running GitOps CI/CD via ArgoCD, plus leading an AWS IAM-to-Identity Center migration using CloudTrail-driven least-privilege role design. Also built and debugged a Kafka/SnapLogic bidirectional pipeline syncing Redshift and HBase, resolving missing-record issues via Kibana-driven investigation.”

Srikanth Reddy

Screened

Mid-level AI/ML Engineer specializing in GenAI and financial risk & compliance analytics

Plainsboro, NJ7y exp

“Built and deployed a production LLM-powered financial risk and compliance platform to reduce manual trade exception handling and speed up insights from regulatory documents. Implemented a LangChain multi-agent workflow with structured/unstructured data integration (Redshift + vector DB) and emphasized hallucination reduction for regulatory safety using Amazon Bedrock. Strong MLOps/orchestration background across Kubernetes, Airflow, Jenkins, and monitoring/testing with MLflow, Evidently AI, and PyTest.”

Bandla Sai Giridhar

Screened

Mid-level Software Engineer specializing in full-stack and cloud-native microservices

Dallas, TX4y exp

“Backend engineer who built a Python/Flask system for high-volume healthcare claims processing, using PostgreSQL as the source of truth and RabbitMQ workers for scalable async processing. Experienced in SQLAlchemy/Postgres performance tuning, multi-tenant data isolation (including Postgres RLS), and integrating/versioning ML model services (scikit-learn/PyTorch/Hugging Face) with controlled rollouts. Drove measurable performance gains by batching background jobs and adding Redis caching (40% less workload; response times cut from ~10s to 2–3s).”

Sai Swetha Bodlapati

Screened

Senior Data Engineer specializing in Spark, Kafka, and Databricks Lakehouse platforms

Dallas, TX5y exp

“Data engineer at Fidelity who built and operated a real-time financial transactions lakehouse on AWS/Databricks, processing millions of records daily with Kafka streaming. Demonstrated strong reliability and data quality practices (watermarking, idempotent Delta writes, validation/reconciliation, observability) and delivered measurable improvements (~30% faster jobs and ~30% fewer data issues) while enabling trusted gold-layer analytics for downstream teams.”