Vetted pytest Professionals

“Software developer with a one-year Philips co-op who has already applied AI-assisted coding in production, not just side projects. Stands out for using multi-agent development setups with task-specific sub-agents and a clear human-led orchestration philosophy focused on context, quality control, and security.”

“Engineer with a process-driven approach to AI-assisted software development, focused on orchestrating where AI adds value while maintaining human review and verification. Has applied this in backend work such as an S3-based invoice pipeline and used multi-agent workflows to speed up large API refactors across many endpoints.”

“Backend-focused engineer who uses AI tools pragmatically for rapid development while staying deeply attentive to concurrency, deadlocks, and system reliability. Built foundational AI retrieval architecture for MastadonTutorAI using ChromaDB and RAG pipelines, and is especially interested in applying multi-agent systems to automate complex backend testing and root-cause analysis.”

“Backend-oriented software engineer who uses AI as a disciplined engineering accelerator rather than a replacement for judgment. They have hands-on experience coordinating multiple AI tools/agents across code generation, testing, debugging, and review, with strong emphasis on security, scalability, and human validation before production use.”

“Series A startup engineer with broad full-stack ownership across backend, data, and frontend, including a real-time ingestion platform that scaled to 10x higher daily volume without downtime while cutting latency from minutes to seconds. Brings strong fintech and B2B SaaS experience building auditable, high-throughput systems for analysts, operations, and compliance teams in regulated environments.”

“AI engineer who deployed a production LLM-powered safety system for an education platform, combining rule-based checks, multi-LLM verification, and selective context (prompt+image vs image-only) to prevent explicit prompts/images from getting through. Strong focus on reliability via benchmarking, trace-based failure analysis, and continuous improvement driven by stakeholder feedback and manual review.”

“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.”

“QA tester with end-to-end ownership of feature/module quality across the full development lifecycle (kickoff through release validation), using Jira/TestRail and disciplined triage workflows. Cites catching a critical data mismatch before release and a reproducible HUD/UI update defect supported by video and system logs; has not yet shipped a AAA title but has comparable production QA processes.”

“AI engineer with hands-on production experience building an end-to-end RAG system that reduced document-answering time from hours to minutes, improving accuracy through chunk overlap and hybrid BM25+semantic retrieval. Also built a LangGraph-based agent that researches company financial news via web search (Google Serper), using Pydantic structured outputs and checkpointing for reliability; experienced collaborating with non-technical stakeholders at JPMC and communicating ROI.”

“Backend/platform engineer who has owned end-to-end production systems in financial/claims domains, including a transaction analytics microservice platform processing ~10M daily operations and cutting latency from ~150ms to <70ms. Also productionized an LLM-powered monitoring/alerting capability (Llama 3 + FastAPI) with prompt design, guardrails, and production evaluation, and led monolith-to-microservices modernization on AWS using feature flags and parallel runs.”

“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.”

Bryan West

Screened

Senior Software Engineer specializing in AI, cloud infrastructure, and full-stack development

Chantilly, VA17y exp

“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”

Sai Harshith Tanguturi

Screened

Mid-Level Software Engineer specializing in Java microservices and AWS cloud-native systems

Grapevine, TX3y exp

“Full-stack engineer who has owned customer-critical analytics and course intelligence platforms end-to-end (React/TypeScript + Node/Express + SQL), including an internal self-serve Reporting & Analytics Center adopted by 1,000+ users. Demonstrates strong systems thinking across performance (2× faster heavy reports), reliability (feature flags, testing), and distributed architecture (RabbitMQ microservices with idempotency, DLQs, and correlation-ID observability).”

Uday kumar swamy

Screened

Senior Machine Learning Engineer specializing in MLOps and NLP/GenAI

Chicago, USA9y exp

“Built a production LLM-agent framework for a startup that performs daily financial/trading analysis by combining live market data with internal tools, including a centralized memory module to prevent context drift and reduce hallucinations. Also implemented an Airflow-orchestrated retail price forecasting pipeline deployed to AWS endpoints, scaling parallel workloads via Kubernetes Executor and validating systems with rigorous functional + LLM-specific metrics and cross-team collaboration.”

Harshitha Parupalli

Screened

Mid-level Data Engineer specializing in multi-cloud real-time and batch data pipelines

Jersey City, NJ4y exp

“Data engineer with healthcare domain experience who owned 100M+ record pipelines end-to-end (Kafka/Kinesis/ADF → PySpark/dbt validation → Spark SQL transforms → Snowflake/Power BI serving). Built production-grade reliability practices (Airflow orchestration, CloudWatch/Grafana monitoring, pytest + contract/regression tests, idempotent ingestion/backfills) and delivered measurable improvements: 35% lower latency and 40% better query performance.”

SAITEJA MALLEMPUDI

Screened

Senior Data Scientist and AI/ML Engineer specializing in GenAI and cloud ML

Chicago, IL6y exp

“ML/AI engineer with hands-on experience owning systems from experimentation through deployment and monitoring, including a Bank of Montreal project that improved timely interventions by 12%. Also brings GenAI/RAG experience with evaluation and safety guardrails, plus clinical NLP pipeline work extracting medication data from notes for patient risk prediction.”

Akhila Kannegari

Screened

Mid-level AI/ML Engineer specializing in FinTech and retail ML systems

Alabama, USA4y exp

“ML-focused candidate with strong Wells Fargo experience building production fraud systems and internal GenAI tools for fraud analysts. Stands out for measurable impact in fraud detection—raising recall from 71% to 88%—while also demonstrating hands-on depth across streaming infrastructure, MLOps, LLM/RAG implementation, and Python service architecture.”

Jessy Kattupalli

Screened

Mid-level Full-Stack Java Developer specializing in enterprise web applications

West Haven, CT4y exp

“Backend/full-stack engineer with hands-on experience building enterprise-scale real-time log analysis platforms using Spring Boot, Kafka, React, and observability tooling. Stands out for using AI tools heavily but responsibly—treating them as accelerators while relying on rigorous testing, architectural review, retry/DLQ patterns, and monitoring to ensure production reliability.”

“Built and iterated an end-to-end virtual waiting room for a real-time ticketing prototype, making concrete architecture tradeoffs (polling + Redis Pub/Sub) and improving performance post-launch with Redis caching (+30% throughput, -15% p99 latency). Also has hands-on experience building Spark/HDFS ETL pipelines with strong reliability/observability patterns and running disciplined NLP model evaluation loops on review-rating classification.”

“Backend/platform engineer from Dune Security with strong experience turning messy, fragmented workflows into reusable production systems. They’ve built a shared database abstraction layer, integrated multiple enterprise security platforms into a unified workflow, and shipped AWS Bedrock-powered security insight features with guardrails and human review.”

“Backend/AI engineer who has shipped an LLM-powered enterprise support-ticket agent at Comcast, building a production-grade microservices pipeline (FastAPI, SQS, Redis) with strong observability (OpenTelemetry/Splunk/Prometheus/Grafana) and reliability patterns (async, caching, circuit breakers, idempotency). Demonstrated quantified impact at scale—processing 10k+ tickets/day while improving response SLAs and routing accuracy through evaluation and human feedback loops.”

“Data/analytics-focused engineer who builds end-to-end KPI reporting and validation products used daily by plant leads and leadership to track yield, downtime, and defects. Combines Python/SQL + Power BI data pipelines with strong data-quality practices (automated validation, monitoring/alerts) and has experience designing scalable frontend architecture in TypeScript/React and working in distributed/microservices-style data systems.”

“Runs two lean real estate companies remotely by building local on-the-ground contact networks and leveraging free-tier technology to keep total annual business costs under $100. Brings a cost-elimination and MVP/validation-first mindset, preferring to join an established company unless a clearly viable business idea emerges.”