Vetted pandas Professionals

“Backend engineer who evolved the X-Ray gaming analytics platform, leading a zero-downtime MongoDB→AWS DocumentDB migration with dual-write, checksum-based validation, and Kubernetes canary rollouts while maintaining real-time monitoring for millions of concurrent sessions. Strong in FastAPI/Python API scaling and performance tuning (cut latency from ~2s to <150ms and reduced DB load 90%) plus production-grade auth/RLS security patterns (JWT, Supabase Auth, PostgreSQL RLS).”

Surya Teja

Screened

Mid-level Backend/Full-Stack Engineer specializing in AI and FinTech payments

Tempe, AZ4y exp

“Full-stack engineer who has owned an operational reporting/dashboard product end-to-end—building a React UI, designing/implementing FastAPI services, and deploying/operating on AWS. Demonstrates strong performance engineering (Postgres query/index tuning using EXPLAIN ANALYZE) with concrete impact (reports reduced from tens of seconds to a few seconds) and a reliability mindset across observability, migrations, and resilient third-party/ETL integrations.”

Deekshit Myakala

Screened

Mid-level Software Engineer specializing in cloud automation and data/ETL platforms

Arlington, Virginia6y exp

“Backend engineer with AWS multi-region production experience building APIs and workflow automation for data center/storage hardware operations (firmware orchestration, maintenance checks, ticketing, dashboards). Also shipped an internal AI chat tool that parses hardware runbooks and incorporates user feedback to retrain the model, and has a strong testing/quality discipline (95%+ coverage) plus database performance tuning via indexing and query monitoring.”

Kevin Allen

Screened

Senior AI/ML Engineer specializing in conversational and generative AI

Austin, TX12y exp

“Built and productionized an LLM-based support assistant end-to-end, including RAG, APIs, monitoring, guardrails, and agent feedback loops. Stands out for translating GenAI prototypes into reliable production systems with structured evaluation, safety controls, and reusable Python infrastructure that improved both support quality and engineering velocity.”

“Engineer with hands-on experience across backend, full-stack, cloud, and AI/ML systems, with particular depth in Python, FastAPI, AWS Bedrock, SageMaker, and RAG-based architectures. Stands out for treating AI and agents as accelerators within disciplined production engineering, emphasizing guardrails, observability, latency/cost monitoring, and scalable system design.”

“Research Extern at Google DeepMind and former AWS Software Development Engineer Intern with a strong focus on practical, trustworthy AI engineering. Built a multi-agent RAG system for personalized news headline generation using a fine-tuned Flan-T5 model, parallel critic agents, FAISS retrieval, and style embeddings, while also leading a 3-person team on the project.”

“Backend-leaning full-stack engineer with experience at Liberty Mutual and Airbnb, building high-scale insurance claims systems (1M+ monthly transactions) and consumer booking/pricing services (120K–180K daily requests). Strong in transactional data integrity, PostgreSQL performance tuning, and production operations (Docker/Jenkins/AWS), with measurable UX/performance wins including ~2.3s page loads and significant runtime failure reduction.”

“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”

Jacqueline Zhang

Screened

Mid-level Machine Learning Engineer specializing in LLMs, fairness, and healthcare ML

Illinois, USA4y exp

“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”

Saikrishna Swaminathan

Screened

Entry-level Aerospace/ADCS Researcher specializing in spacecraft controls and simulation

Kanpur, India1y exp

“Robotics/control-focused candidate with hands-on ROS2 + Gazebo experience implementing MPC with online state identification on a Crazyflie drone, including camera-based position determination fixes. Also worked on multi-agent spacecraft formation control and constellation optimization, debugging numerical drift and redesigning leader-follower control laws to handle delayed/outdated updates; uses Docker to ensure reproducible simulation results across machines.”

“Built and shipped a production agentic RAG system for healthcare analysts that automated compliance/operations knowledge retrieval across PDFs, reports, and databases. Emphasizes production reliability (monitoring, retries, fallbacks, async queues), strong evaluation/iteration loops, and measurable impact (3–10s responses and ~98% top-k retrieval accuracy).”

“Built a production AI-powered workflow automation system at Netflix that integrated OpenAI and LangChain with FastAPI services on AWS, cutting roughly 320 hours of manual operational effort. Brings a mix of full-stack product development and practical AI systems experience, with strong attention to reliability, maintainability, and non-technical user adoption.”

“Backend/data engineer with hands-on production AWS experience spanning serverless APIs (Chalice/Lambda/API Gateway/Cognito) and data pipelines (Glue PySpark + Step Functions). Has modernized a legacy SAS reporting system into AWS microservices and implemented schema-drift detection and incident prevention for ETL workflows, plus measurable SQL tuning wins (30 min to <10 min runtime).”

“Internship experience at Intuit building an LLM-grounded QA system for internal microservice data across 100+ microservices, using a graph database approach (evaluated Neo4j and selected AWS Neptune for production alignment). Also has UC Berkeley research experience (including work with Prof. Dawn Song / Berkeley Eye Research Lab) and cross-functional collaboration with bioinformatics/biology teams to deploy software systems on research servers.”

“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”