Vetted Unit Testing Professionals

Meghana Divve

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native enterprise applications

Tampa, FL6y exp

“Built and launched a production internal AI support assistant at CompuCom, focused on reducing time spent searching across systems by combining retrieval, internal tool use, and grounded LLM responses. Stands out for pragmatic zero-to-one execution: scoped the product in phases, prioritized safety over premature autonomy, and iterated using real user feedback to improve relevance, usability, latency, and cost.”

Romain Delamare

Screened

Senior Full-Stack Software Engineer specializing in microservices and web applications

Montpellier, France10y exp

“Developer who treats AI as a junior collaborator, using it to accelerate mobile app feature development and UI/UX iteration while retaining architectural and implementation ownership. Has hands-on experience with specialized agents, multi-agent collaboration, and supervisor-agent patterns, suggesting practical fluency in AI-native development workflows.”

“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

“Full-stack engineer at an early-stage startup (AirKitchenz) who owned the hourly booking/availability and first paid booking flow end-to-end—React/TypeScript frontend, Node backend, Postgres modeling, and Stripe payments/webhooks. Experienced operating production on AWS (EC2/Elastic Beanstalk, Docker, RDS, CloudWatch) and building reliable, idempotent integrations while iterating quickly in a pre-PMF environment through direct host/renter feedback.”

“Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).”

“Backend/data engineer with production experience in financial payroll, tax, and compensation platforms, building Python microservices and AWS-based data pipelines for high-volume, peak-driven workloads. Strong reliability focus (OAuth2 auth, retries/timeouts, structured logging, incident response) and proven performance wins, including cutting complex report queries from ~8 minutes to under 30 seconds.”

“Backend/data engineer with hands-on production experience building a real-time notification API on Flask/Celery/Postgres and scaling it on AWS with Docker, Redis queuing, and SQLAlchemy query optimization. Also delivered AWS serverless deployments (Lambda) using Terraform + GitHub Actions and built AWS Glue ETL pipelines from S3 to Redshift with CloudWatch monitoring and DataBrew data quality checks.”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“New-grad backend engineer who built a real-time genome analysis pipeline, replacing a slow batch system with an event-driven distributed architecture in Python/Redis and a React progress dashboard. Reports ~6x improvement and cutting analysis time from days to hours with zero data loss under peak load, emphasizing reliability patterns like retries and idempotency plus API security (JWT/RBAC/HTTPS).”

“Backend/AI engineer with internship experience building an AI-powered financial insights platform (FastAPI, Redis, BigQuery) and prior HCL experience leading a monolith-to-microservices refactor (Flask, Kafka) using blue-green deployments. Demonstrates strong performance/security focus (OAuth/JWT/RBAC, encryption) and measurable impact on latency, downtime, and ML model reliability; MVP was submitted to Google’s accelerator program.”

“AI/LLM engineer who has shipped production RAG chatbots using LangChain/OpenAI with FAISS and FastAPI, focusing on real-world constraints like context windows, concurrency, and latency (reported ~40% latency reduction and <2s average response). Experienced orchestrating AI pipelines with Celery and fault-tolerant long-running workflows with Temporal, and has applied NLP model tradeoff testing (Word2Vec vs BERT) to drive measurable accuracy gains.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“Designed and deployed end-to-end LLM-powered AI agent systems to automate knowledge-intensive workflows across marketing/GTM, recruiting, and support. Brings production reliability rigor (evaluation pipelines, monitoring, testing, A/B experiments) plus orchestration expertise (Airflow, Prefect, custom Python) and a track record of translating non-technical stakeholder goals into working AI solutions (e.g., personalized customer engagement agent at Lara Design).”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“Software engineer experienced delivering customer-facing, real-time industrial monitoring dashboards (motors/shafts/turbines) by partnering directly with end users to refine charts, alerts, and performance. Strong in API/platform integrations and production troubleshooting—uses feature flags, logging, validation/mapping, containerization, and performance testing to keep systems stable while iterating quickly.”

“DevOps engineer (State Farm) with hands-on ownership of Python backend services and data pipelines, deploying microservices and workers on Kubernetes using GitOps (Argo CD). Has led complex cloud-to-on-prem/hybrid migrations with staged cutovers and rollback planning, and built Kafka-based real-time streaming pipelines with schema governance, autoscaling, and strong observability.”

“Backend engineer who built a Node.js + SQL service integrating with the Google Ads API to periodically upload online and offline conversions via Azure Logic Apps, persisting upload records for ROI reporting. Implemented PII hashing, token validation, redundancy, and detailed failure/status logging for reliability and debuggability. Currently scoping an LLM/agent workflow (likely LangChain) to let marketing bulk-update e-commerce product data using SEO keywords without developer involvement.”

“Internship experience at Paycom owning an end-to-end personalized course recommendation feature for an LMS, spanning SQL-based data pipelines, ML integration, and FastAPI REST services for real-time recommendations. Focused on production tradeoffs (latency vs. accuracy), scaling/SQL optimization, and post-launch iteration driven by engagement metrics.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

“AI/LLM engineer who shipped a production RAG-based knowledge assistant at SparkPlug serving 10,000+ daily users, streaming GPT-4 answers with inline citations over WebSockets. Demonstrated measurable impact (support resolution time cut 18→12 minutes; retrieval precision +~20%) and strong production rigor across ingestion, monitoring/alerting, evaluation, and messy ERP-style data integration with validation, RBAC, and idempotent operations.”

Sonam Chhatani

Screened

Mid-level AI Engineer specializing in causal inference and LLM research

New York, USA8y exp

“LLM engineer who has deployed a production system combining LLMs with causal inference (DoWhy) to enable counterfactual “what-if” analysis for experimental research, including a robust variable-mapping/validation layer to reduce hallucinations. Also partnered with non-technical operations leadership at Irriion Technologies to deliver an AI-assisted onboarding workflow that cut onboarding time by 50% and reduced manual errors by ~40%.”

Mohammed Mudassir Hussain Shaikh

Screened

Entry-Level Full-Stack Software Engineer specializing in serverless AWS and AI applications

Tempe, AZ1y exp

“Built and deployed serverless AWS applications (Lambda/S3/RDS Proxy) including a NASA L’Space React + Python data analysis tool, focusing on performance for large datasets. Demonstrates strong cloud troubleshooting across compute and networking (CloudWatch-driven diagnosis, EC2 scaling, security group fixes) and a user-driven iteration loop that improved product usability with dynamic filtering and interactive UI.”

Hari Krishna Kona

Screened

Mid-level AI/ML Engineer specializing in Generative AI and LLM-powered NLP

Boston, MA3y exp

“LLM/AI engineer who built a production automated document-understanding pipeline on Azure using a grounded RAG layer, designed to reduce manual review time for unstructured financial documents. Demonstrates strong real-world scaling and reliability practices (Service Bus queueing, Kubernetes autoscaling, observability, retries/circuit breakers) plus rigorous evaluation (shadow testing, replaying traffic, multilingual edge-case suites) and stakeholder-friendly, evidence-based explainability.”