Vetted Terraform Professionals

Siva Harini Sri Janaki Raman

Screened

Mid-level Data Engineer specializing in cloud data platforms

Dallas, TX3y exp

“Built an AI-powered internal support assistant at CVS Health using GPT-4, LangChain, and Pinecone, applying RAG, validation, and monitoring to reduce repetitive support tickets while protecting sensitive healthcare data. Stands out for a pragmatic approach to AI engineering: using multi-agent and LLM workflows to accelerate development while keeping systems constrained, observable, and production-friendly.”

Aditya Rao

Screened

Mid-level Software Engineer specializing in backend, AI, and distributed systems

San Jose, CA5y exp

“Software engineer with 4.5 years of startup experience across programmatic advertising, health tech e-commerce, and automobile diagnostics, plus both bachelor's and master's degrees in CSE. Built an agentic global supply chain platform in a hackathon using a highly structured AI-first workflow, and has hands-on experience designing multi-agent debate systems, rollout safeguards, and observability-driven production fixes.”

Anuja Nayak

Screened

Junior Software Engineer specializing in distributed systems and reliability

Seattle, WA3y exp

“Oracle engineer focused on reliability and internal platform tooling, with hands-on experience automating regional traffic failover and building an LLM-assisted incident investigation workflow. Stands out for owning production-impacting systems end-to-end and delivering measurable operational gains, including cutting failover recovery to under five minutes and reducing incident triage from hours to minutes.”

“Veteran software engineering leader who started as an early internet engineer in the mid-1990s and has since grown into Director/VP-level leadership across legacy web platforms, logistics systems, and modern data engineering. Particularly compelling for companies needing a hands-on leader who can modernize complex Perl/UNIX monoliths, manage large cross-functional teams, and deliver operational systems in warehouse, marketplace, and reverse-logistics environments.”

“Enterprise platform engineering leader who identified platform engineering as a major opportunity at Kyndryl and built an entire internal practice around it by codifying the offering and evangelizing it across leadership. Now exploring founding an agentic AI developer platform aimed at reducing variance and improving consistency in building/deploying cloud-native applications; has not raised capital yet.”

“Backend-focused engineer at Wells Fargo owning production payments features end-to-end, including Spring Boot REST services, CI/CD + containerized AWS deployments, and CloudWatch-based observability. Has hands-on experience stabilizing high-traffic transaction workflows and building reliable ingestion/integration flows using idempotency, retries/backoff, and reconciliation.”

“Insurtech R&D leader turned CTO with 25+ years across telecom, DoD, automotive, and commercial insurance. Built patented AI-driven insurance document ingestion (>95% accuracy with 1–3 samples) and led Azure-based backend/service development while managing up to 15 engineers at a commercial P&C MGA that reached a ~$60M book of business (~30 loss ratio) before run-off due to paper issues. Known for hands-on discovery (including field inspections) and rapid MVP delivery, including a risk-inspection iPad app that auto-generated draft reports.”

“Backend/data engineer with production experience building a SaaS analytics platform: FastAPI-based microservices with Redis caching and reliability patterns (RBAC, retries/backoff, centralized error handling). Also delivered AWS data pipelines (Glue/PySpark to Redshift) and owned real production incidents using CloudWatch/SNS, plus hands-on PostgreSQL query tuning on multi-million-row reporting workloads.”

“Backend engineer with experience at Razorpay and Groww, focused on hardening high-throughput financial systems for reliability and low tail latency through incremental improvements (SQL/index tuning, Redis caching, timeouts, idempotency). Also built/refactored a commodity risk tracker using Supabase Auth + Postgres RLS for strict per-user isolation, with a strong emphasis on API contracts, observability, and safe migrations.”

“Software engineer with a CS/Computer Engineering background who has worked on ML/NLP (Hugging Face, clinical NLP, text generation and structured extraction) and has a school robotics project integrating a trained ML model with microprocessor-controlled hardware to drive motor movement and writing. Currently focused on building and deploying applications and ML models to AWS/Azure using Docker, Kubernetes, and CI/CD; targeting ~$150K compensation.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“JavaScript/TypeScript engineer with proven open-source impact: delivered a major reliability upgrade to a retry/error-handling library (standardized typed errors, added exponential backoff, expanded Jest tests, and implemented GitHub Actions CI) that was merged and released. Demonstrates strong performance engineering and debugging skills (profiling-driven optimization; diagnosed a race condition causing inconsistent retries) plus a documentation-first approach to developer experience.”

“Backend engineer who has delivered large, measurable performance wins (10x throughput, 67% latency reduction) by combining Flask microservices, Redis caching, and AWS autoscaling/observability. Has hands-on depth in SQLAlchemy/Postgres optimization and production scaling pitfalls (cache consistency, connection exhaustion), plus experience deploying real-time ML inference (XGBoost) on AWS Lambda and building secure multi-tenant Kubernetes isolation.”

“Software engineer with production experience building a serverless monolith and multi-layer video pipeline at easyML, plus hands-on integration of multiple LLM providers (Grok/Claude/OpenAI) into a full-stack app. Interested in robotics via computer vision (OpenCV/OpenMMLab), with a strong real-time systems mindset around SLOs, latency, determinism, and reliability; also has low-level OS experience writing a keyboard device driver.”

“Full-stack Java developer with IBM and Epic Systems experience modernizing legacy enterprise apps into microservices and delivering customer-facing healthcare claims workflows at very high scale (2M+ transactions/day). Strong blend of product engineering (APIs + React/TypeScript UI) and production operations on AWS, including performance incident remediation via query optimization, indexing, and autoscaling.”

“Built and scaled a multi-agent LLM automation pipeline during a fintech internship, growing from a rapid 1-week proof-of-concept to a 15+ agent hierarchical system that cut market brief report generation time from ~5 hours to under 30 minutes. Hands-on with agent frameworks (Haystack, CrewAI, LangChain) and experienced in debugging agent communication issues via sandboxed modular testing and context/token management; also regularly gives architecture-first technical demos at multiple hackathons and university events.”

“Infrastructure/DevOps engineer (Vidmob) focused on AWS + containers, owning GitLab CI/CD and Terraform-managed environments. Led a high-impact CI incident by correlating runner queue time, Docker pull latency, and NAT egress; implemented ECR pull-through caching and VPC endpoints to restore performance and then standardized the fix in Terraform for future scale-ups.”

“Built and deployed a production compliance automation RAG system (at Citi) that generates citation-backed, schema-validated risk summaries for regulatory document review. Emphasizes regulated-environment reliability with retrieval-only grounding, abstention, confidence thresholds, and immutable audit logging, plus orchestration using LangChain/LangGraph and Airflow. Reported ~60% reduction in compliance review effort while maintaining high precision and traceability.”

“GenAI/ML engineer who has shipped production agentic systems in highly regulated and high-throughput environments, including an AWS Bedrock-based fraud/compliance workflow at U.S. Bank with PII redaction and hallucination detection that cut investigation time by 50%+. Also built and evaluated RAG and recommendation systems at Target, using RAGAS-driven testing, hybrid retrieval with re-ranking, and SHAP explainability dashboards to align model behavior with merchandising business KPIs.”

“Backend-focused engineer with production experience building Spring Boot services for automated workflow and data-processing platforms, using queues plus retry and idempotency patterns. Also uses Python to automate data processing; emphasizes testing and peer review for maintainability.”

“Built and shipped a production LLM-powered internal operations automation platform using LangChain RAG (Pinecone) and FastAPI microservices, deployed on AWS EKS, serving 10k+ daily interactions. Implemented a rigorous evaluation/observability stack (golden datasets, prompt regression tests, MLflow, retrieval metrics, hallucination monitoring) that drove hallucinations below 2% and improved reliability, and partnered closely with non-technical ops leaders to cut manual lookup work by 60%+.”

“Built a production internal LLM-powered knowledge assistant using a RAG architecture (Python, LLM APIs, cloud services) that answers employee questions with sourced, grounded responses from internal documents. Demonstrates strong practical depth in retrieval tuning (chunking/metadata filters), orchestration with LangChain, and production reliability practices (latency optimization, automated embedding refresh, evaluation metrics, logging/monitoring) while partnering closely with non-technical operations teams.”

“Software engineer with hands-on ownership of both fintech checkout improvements (saved payment methods/one-click checkout with tokenization and feature-flag rollouts) and production LLM/RAG systems for customer support. Demonstrates strong operational rigor via guardrails, evaluation loops integrated into CI/CD, and scalable data pipelines handling messy PDFs/CSVs/logs with reliability and observability.”

“Distributed-systems engineer applying robotics-style patterns to software: built "Vibecheck," a high-throughput real-time video + OS-telemetry fusion and analysis system (500+ MB/session) with strict latency constraints. Strong in containerization and CI/CD (Docker, GitHub Actions) and in designing fault-tolerant, event-driven architectures (Kafka/RabbitMQ), plus hands-on debugging of multi-agent coordination using blackboard + watchdog/circuit-breaker control patterns.”