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Vetted Kubernetes Professionals
Pre-screened and vetted.
Yuan-Hao Chen
Intern Software Engineer specializing in backend systems and cloud infrastructure
“Backend-focused intern who owned real-time livestream features: live comment moderation using AWS Comprehend (sentiment/toxicity/PII) with safe fallbacks, plus AI-generated positive commentary via AWS Bedrock (Claude 3 Haiku). Emphasizes reliability/low-latency design, IAM troubleshooting, and disciplined GitOps-style CI workflows for reproducible deployments.”
Aashna Kunkolienker
Junior AI Engineer specializing in agentic workflows and ML platforms
“Building a production LLM/agent system for a leading US dental provider that extracts rules from payer handbooks/portals and EDI 271 responses to validate and improve patient cost estimates. Combines GCP stack (BigQuery, GKE, Cloud Run, Pub/Sub, Vertex AI) with strong agent reliability practices (observability, validator agents, grounding, PII/hallucination guardrails, confidence scoring) and has led non-technical customer stakeholders on enterprise ServiceNow↔Aha sync and AI-powered enterprise search/summarization.”
Mithilesh Gaurihar
Mid-Level Software Engineer specializing in Java microservices and cloud-native systems
“Full-stack engineer (SAP Labs experience) who built an end-to-end, real-time fraud detection system on Java 11/Spring Boot microservices with Kafka event streaming and a React/Redux analytics dashboard with WebSocket updates. Demonstrated strong production ownership by diagnosing a critical memory leak with Prometheus/CloudWatch + heap dumps and improving performance with Redis caching (40% faster queries), while also modernizing deployments via Kubernetes, Jenkins CI/CD, and Terraform.”
Chia-En Lu
Junior AI/ML Systems Engineer specializing in LLM infrastructure and distributed training
“Built and shipped a production NMT system translating medical documentation for a rare/low-resource language, tackling data scarcity with retrieval-driven pattern matching plus dictionary/grammar- and LLM-based augmentation and validating quality with a linguistic expert. Also develops agentic LLM workflows with LangChain/LangGraph (including a deep-research style system) and has experience aligning medical AI deployments with clinician-defined risk metrics and human-in-the-loop decision making.”
Advitha Bawgi
Junior Full-Stack Software Engineer specializing in cloud-native microservices
“Backend engineer with hands-on IoT and AI product work: built a decoupled Raspberry Pi + AWS IoT Core weather monitoring backend and a Dockerized FastAPI LLM service on AWS ECS using OpenAI/HuggingFace with an emerging RAG layer. Also delivered measurable performance gains at DAZN by redesigning event-driven/serverless ingestion (SNS, S3->Lambda->DynamoDB), cutting latency ~30% and boosting throughput ~25% while automating ~90% of manual sync work.”
Niteesh Ganipisetty
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and Computer Vision
“Built an LLM-powered learning assistant (EduQuizPro/EduCrest Pro) that uses RAG over URLs and PDFs to generate quizzes, notes, and explanations for students/professors. Emphasizes production robustness—implemented dependency fallbacks (FAISS/Sentence Transformers/Gradio), CLI-safe mode, and NumPy-based indexing—along with a custom orchestration layer to keep multi-step AI workflows reliable.”
Prateek Patil
Engineering Leader specializing in Digital Health, AI, and Cloud Platforms
“Senior Engineering Manager at Roche leading two Scrum teams building internally shared (“inner-sourced”) tools and libraries for a healthcare enterprise. Has led security/compliance-first architecture decisions (e.g., Python AI modules running inside a Java container) and front-end modularization (Angular monorepo to module federation), with a strong focus on developer experience via automated Swagger/OpenAPI documentation and robust testing/versioning practices.”
Mounika Gunturu
Senior Python Full-Stack Developer specializing in cloud-native microservices and data platforms
“Backend/data engineer from Oliver Wyman who built and ran production Python (FastAPI) services on AWS (ECS/Lambda/API Gateway) supporting risk modeling and regulatory reporting. Strong in reliability/observability, Glue-based ETL with data quality controls, and legacy SAS-to-Python modernization with rigorous parity validation; also demonstrated measurable SQL performance wins and cost-control improvements in serverless scaling. Based in Raleigh, NC and can travel onsite for important Bethesda-area meetings.”
silin liu
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and enterprise ML systems
“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”
Jahnavi Bellapukonda
Mid-Level Software Engineer specializing in full-stack web and cloud systems
“Full-stack engineer with strong data engineering and privacy-domain experience, having owned an automated Data Subject Rights (DSR) processing pipeline end-to-end across Azure SQL and GCP (GCS/BigQuery). Emphasizes production reliability via idempotency, validation checkpoints, structured logging/monitoring, and safe CI/CD-driven deployments, and has also built React+TypeScript + Node/Postgres web apps with scalable, maintainable architecture.”
Jainum Sanghavi
Mid-level DevOps Engineer specializing in cloud automation and Kubernetes platforms
“Robotics/ML engineer who has built SO(3)-equivariant models for robotic manipulation, including custom equivariant layers and differentiable point-cloud rasterization/derasterization workflows. Also brings 2 years of DevOps experience in banking systems, automating CI/CD and infrastructure at scale (managed 180 OCI servers; reduced rebuild downtime by 80%).”
Swathi Sankaran
Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI
“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”
Shravya Shashidhar
Intern Software Engineer specializing in LLM agents and full-stack development
“Embedded C++ engineer with Bosch automotive infotainment experience, owning real-time audio middleware modules with strict latency/memory constraints. Strong in profiling/optimizing deterministic behavior, debugging hardware-specific intermittent issues, and building automated test + CI pipelines; currently ramping up on ROS2 concepts (DDS, nodes/topics/services) to transition toward robotics.”
“Built and productionized an AI-native, agentic appeals decisioning system for health insurance operations, automating 500k+ scanned appeals/year. Delivered measurable impact by cutting review time from 12–15 minutes to ~3 minutes and auto-resolving ~85% of cases with strong auditability, evaluations, and human-in-the-loop guardrails, deployed as containerized microservices on Azure AKS.”
Charlotte Yu
Junior Full-Stack AI Engineer specializing in LLM apps and RAG systems
“Built and shipped a production LLM-powered “Vet agent” that automates pet symptom intake across multimodal inputs (images/files/text/speech) and provides analysis/home-care guidance, reaching thousands of daily active users within two months. Demonstrates strong agent engineering fundamentals: state-machine orchestration with structured JSON, tool/schema validation, high-availability routing/failover, and rigorous offline/online evaluation loops with trace-driven reliability improvements.”
Adrian Alvarez
Principal Cloud & Infrastructure Engineer specializing in reliability and regulated data platforms
“Founder/CTO-type startup leader who has built cloud-native data and AI platforms from scratch while owning both technical vision and product direction. Brings rare end-to-end startup experience spanning zero-to-one building, growth-stage execution, and fundraising from early stage through exit, with a strong ability to translate technical complexity into clear investor narratives.”
Fatim Majumder
Junior ML research engineer specializing in evaluation platforms and applied machine learning
“ML/LLM infrastructure engineer who built and shipped a production internal evaluation + failure-analysis agent (Arthur AI / R3AI context) that orchestrated end-to-end benchmarks with deterministic lineage, regression detection, and root-cause reporting at 5,000+ benchmarks/week. Also built backend observability and data validation systems for analytics pipelines at FullStory processing ~3.4B weekly events, emphasizing schema validation, quarantine fallbacks, and idempotent operations.”
Abhishek Adinarayanappa
Junior Software Engineer specializing in backend, cloud, and machine learning systems
“Built Digipulse, a university project that ingested and clustered Bluesky tweet data at scale and used Gemini to generate near-real-time topic summaries, processing 1M+ tweets per day. Also brings Intel experience with Prometheus and Kubernetes, including production monitoring and incident troubleshooting.”
Zahra Shergadwala
Junior Machine Learning Engineer specializing in AI, computer vision, and data systems
“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”
Darshan Patel
Mid-level Data Engineer specializing in financial and trading data
“Quant Data Engineer at ASX who is also building FinishKit, a full-stack SaaS that scans AI-generated codebases for bugs and production-readiness issues. Combines React/TypeScript, Supabase/serverless, Fly.io, and Postgres with strong product instincts, rapid iteration, and prior experience building secure multi-tenant data and dashboard systems across enterprise teams.”
Vidhi Chhatbar
Mid-level Full-Stack Engineer specializing in cloud microservices and FinTech
“Software engineer with experience across enterprise (AIG, MSCI) and an early-stage startup (Job Map), owning production systems end-to-end. Built secure insurance microservices on Spring Boot with JWT/RBAC and AWS-based CI/CD/observability, plus Kafka streaming pipelines for financial data. Also shipped a GenAI personalization MVP using FastAPI and LLM APIs in a high-ambiguity startup environment.”
Aditi Garg
Mid-level Software Engineer specializing in distributed systems and AI-powered platforms
“Software engineer with experience spanning an SEL internship and Walmart, combining backend/data pipeline work (Python, Kafka, relational DBs) with DevOps practices (Docker, Grafana, GitHub/Jenkins CI/CD, GitOps). Notably contributed to a REST-to-GraphQL migration aimed at reducing cloud utilization and implemented testing strategies to validate the transition.”
Aarushi Mahajan
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and MLOps
“Internship experience shipping production AI systems: built an end-to-end RAG platform (Python/FastAPI + LangChain/LangGraph + vector search) to answer support questions from unstructured internal docs, with a strong focus on hallucination prevention through confidence gating and rigorous offline/online evaluation. Also delivered an AI-driven personalization/analytics feature using an unsupervised clustering pipeline, iterating with PMs to align statistically strong clusters with actionable business segmentation.”
Omkar Bhambure
Mid-level Software Developer specializing in backend microservices for healthcare and FinTech
“Built and deployed an AI-powered insurance claims fraud platform end-to-end using Java/Spring Boot, Kafka, OpenAI, pgvector, and AWS EKS. Stands out for combining LLM/RAG architecture with production-grade scalability and observability, delivering measurable impact including 62% less manual review, 40% better fraud precision, 37% higher throughput, and 99.95% uptime.”