Vetted pytest Professionals

sampath achalla

Screened

Mid-Level Software Engineer specializing in backend and full-stack web applications

USA4y exp

“Backend engineer focused on scalable, secure, observable systems—built an async workflow backend with REST APIs and state persistence that improved reliability under concurrent load and cut end-to-end processing time ~40%. Strong in production security for multi-tenant systems (OAuth2/JWT, RBAC, DB row-level security) and in low-risk migrations using feature flags and canary releases, including catching and preventing cross-tenant data access issues with CI-based RLS tests.”

Sampath Achalla

Screened

Mid-level Python Full-Stack Engineer specializing in AI microservices and cloud data platforms

USA3y exp

“Backend-leaning full-stack engineer in fintech/payments who shipped an end-to-end Stripe payments + webhook system for a financial microservices platform, emphasizing ledger accuracy via idempotency, transactional writes, retries, and DLQs. Also delivered a real-time React/TypeScript payment status dashboard informed by user interviews, and improved production performance by 35% p95 latency through PostgreSQL tuning and Redis caching on AWS.”

MUNI CHARAN SINGAMALA

Screened

Mid-level Data Analyst/Data Engineer specializing in SQL, ETL pipelines, and BI dashboards

Florida, USA4y exp

“Built and supported a production analytics backend (Python, PostgreSQL/Teradata, Airflow) powering KPI/reporting dashboards, and resolved peak-time latency/timeouts through systematic SQL tuning (EXPLAIN ANALYZE, indexing, query rewrites, pre-aggregations). Also shipped an applied AI-style feature that generates plain-language report summaries from pre-computed metrics with validation, monitoring, and fallback to manual review.”

Ram Abhinav Vedant Madabushi

Screened

Junior Full-Stack/AI Engineer specializing in web platforms and LLM applications

Palo Alto, CA2y exp

“Backend engineer from FoodSupply.ai who built and evolved a scalable restaurant/supplier product and order management platform using Node.js and REST APIs. Implemented a hybrid MySQL+MongoDB data architecture, optimized performance with Redis/Prisma, and led a phased migration with feature flags and a temporary sync layer to maintain data consistency. Strong focus on production security (OAuth2, RBAC, row-level security, AWS IAM) and reliability practices (testing with Pytest, Docker/AWS pipelines).”

“Software engineer with hands-on experience across platform modernization, production AI agents, and workflow automation. They led a monolith-to-microservices migration that increased deployment speed from weekly to daily, built a self-healing GPT-powered browser agent with an 85% autonomous recovery rate, and founded/ran ZapDash, where they hardened Kafka-based integrations against silent data loss.”

“Built and shipped "OnCall Copilot," a production Slack-based RAG assistant that answers on-call questions from runbooks and postmortems with citations using a FAISS vector index. Emphasizes reliability and measurable performance via strict guardrails ("no evidence, no answer"), evaluation metrics, drift monitoring, and operational hardening with Docker, logging, health checks, and offline fallback.”

“Self-taught frontend developer with React/TypeScript project experience and strong QA background. Contributed as a QA tester on a Skunkworks web app by refining large story tickets, defining happy-path/edge test cases, and setting sprint metrics; also improved a legacy PHP web app by modularizing SFTP bulk upload code and enhancing page navigation.”

“Built and deployed a production AI accessibility platform that turns chart and image-based graphs into real-time audio narratives for visually impaired users. Implemented a ResNet-based CV + OCR + NLP + TTS pipeline and improved performance through preprocessing, Redis caching, and Kubernetes autoscaling/rolling updates on AWS to handle traffic spikes with no downtime.”

“Full-stack engineer with production experience building a legal-tech SaaS onboarding/invitation system end-to-end using Remix/React/TypeScript, Node, Prisma/Postgres, and transactional email (Resend). Emphasizes reliability and operability (idempotent actions, state-machine workflows, layered validation, migrations, logging/metrics/tracing) and has deployed both on AWS (ECS/Fargate, RDS, CloudWatch, SQS) and Dockerized infrastructure (DigitalOcean + Nginx + TLS/Cloudflare).”

Nischit Patel

Screened

Mid-level Software Development Engineer specializing in Python, APIs, and AWS

NC, US5y exp

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

“Built and shipped Site Audit AI, a production multi-turn Claude-based agent that autonomously crawls websites, calls tools, and generates scored audit reports—reducing a manual 2-3 hour developer workflow to under 60 seconds. Also brings practical experience integrating inconsistent payroll/HR data across platforms like QuickBooks and Keka, with a strong focus on validation, fault tolerance, and resumable workflows.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“Backend/AI engineer who built an enterprise RAG chatbot over 40,000+ technical documents, owning the system from ingestion and retrieval design through launch, optimization, and incident prevention. Stands out for treating LLM reliability as a data, retrieval, and observability problem—delivering 90%+ benchmark accuracy, ~50% fewer hallucinations, and major gains in lookup speed and latency.”

“Built production LLM/agent systems for procurement and contract spend controls, including a proactive contract value leakage detection platform that moved an organization from reactive audits to pre-payment rejection. Combines multi-agent orchestration (Semantic Kernel/LangChain/AutoGen), document AI benchmarking (Textract vs Azure DI), and MLOps/testing (MLflow, QTest/Pytest) with strong security practices (RAG-grounded responses to prevent prompt injection). Integrated anomaly alerts directly into SAP SES workflows and Power BI dashboards, citing ~$38M leakage addressed across large spend environments.”

“Built and deployed a production AI-powered personalized learning platform (Django + FastAPI) featuring an LLM+RAG tutoring assistant and automated grading. Demonstrates strong applied LLM reliability engineering (structured JSON outputs with Pydantic validation, hallucination control via FAISS-based RAG thresholds and refusals) plus scalable async microservice design and Airflow-orchestrated ETL across AWS/GCP.”

“Built a production-grade clinical and insurance document AI system in a HIPAA/PHI-regulated environment, taking it from experimentation through Azure deployment, monitoring, and iterative improvement. Stands out for translating RAG/LLM research into reliable microservices with strong safety controls, drift monitoring, and human-in-the-loop workflows that cut manual review time by 60-70%.”

Emma Choudhary

Screened

Senior Full-Stack Software Engineer specializing in Python, Django, and Generative AI

United States6y exp

“Backend/data engineer with hands-on production experience building partner-facing Python APIs (FastAPI, Celery, Postgres/Redis) and AWS serverless data platforms (Lambda, SQS, Step Functions, Glue). Emphasizes reliability and governance—JWT tenant-scoped auth, secrets/config hygiene, data-quality quarantine, and incident ownership—plus measurable SQL tuning that eliminated timeouts and stabilized reporting workloads.”

“ML/AI engineer with hands-on experience building healthcare and fraud-detection systems from experimentation through deployment, monitoring, and retraining. Stands out for combining real-time IoT pipelines, cloud-native MLOps, and GenAI/RAG in regulated healthcare settings, with reported impact including reduced emergency response times and a 25% reduction in manual diagnosis time.”

“Cloud infrastructure/DevOps engineer focused on AWS/Azure production environments, with hands-on experience running EKS-based platforms, Terraform-driven IaC, and secure Jenkins/GitLab CI pipelines. Has led real-world migrations from EC2 to Kubernetes using blue/green cutovers and executed multi-AZ failover testing with documented same-day recovery and no data loss; does not have direct IBM Power/AIX ownership experience.”