Vetted Kubernetes Professionals

Haytham Nasri

Screened

Software Engineering Manager specializing in cloud-native mobility and enterprise Java

Amsterdam, The Netherlands15y exp

“Backend/architecture-focused player-coach who designed and built two partner-facing data APIs using a contract-first (OpenAPI) governance model in Java (Quarkus/Spring Boot). Strong in technical standards, incident response, and delivery systems—improved reliability under peak load (DB/index fix) and sped up delivery by streamlining PR/review processes while maintaining production stability.”

Yang Qi

Screened

Mid-level Full-Stack Software Engineer specializing in AI platforms and data visualization

Cambridge, MA3y exp

“Full-stack engineer with healthcare/bioinformatics experience who built a real-time genomic data analysis and 2D visualization feature (React/TypeScript + D3, FastAPI) at University of Utah Health, deploying on AWS ECS Fargate with monitoring and measuring engagement via Google Analytics. Also built AWS Lambda-based ETL pipelines for lab data ingestion using pandas/NumPy with reliability patterns (idempotency, retries, CloudWatch alerting) and drove maintainability improvements through shared component libraries and React hooks.”

Frank Albanese

Screened

Executive CTO & Engineering Leader specializing in AI/ML and distributed systems

New York, NY10y exp

“Founder of Essence, a wisdom and memory preservation platform with early testing indicating mental health benefits and positive impact for hospice patients. Has raised $25K to date and reports prior capital-raising experience through Y Combinator and the Berkeley Angel Network, with a GTM plan starting in hospice and expanding to the general public.”

Tanisha Pradeep Bhola

Screened

Mid-level Software Development Engineer specializing in backend, cloud, and microservices

Dayton, Ohio4y exp

“Accenture engineer with hands-on experience taking an NLP sentiment analysis system from prototype to production, emphasizing robustness to noisy data, scalability, and observability (dashboards for latency/error/throughput). Also supports customer-facing teams with demos and PoCs, translating client requirements into secure, scalable architectures and troubleshooting LLM/agent workflows via logs and step-level traces.”

Naisarg Halvadiya

Screened

Senior Full-Stack & AI Engineer specializing in scalable web platforms and LLM automation

Bengaluru, India4y exp

“Built a production agentic AI assistant in Python using Playwright plus Google Gemini’s vision capabilities to automatically document and execute UI workflows step-by-step, reducing developer time spent on trivial documentation/knowledge transfer. Also built an Apache Airflow ETL pipeline and has experience evaluating AI agents with human-in-the-loop methods, plus successfully communicated a vision-model-based CMS analytics PoC to non-technical university stakeholders and proposed it to Academic Technology with cost-savings rationale.”

“Senior AI/ML engineer with hands-on experience building production LLM systems in healthcare, including RAG-based clinical question answering and end-to-end MLOps on Vertex AI and Kubernetes. They combine strong platform engineering with applied GenAI work, citing a 35% improvement in factual accuracy and a 30% boost in internal team productivity through modular Python services and CI/CD.”

“ML/AI engineer with strong healthcare insurance domain depth who has owned fraud detection and LLM claims products end-to-end in production. Stands out for combining modern MLOps and RAG architecture with measurable business impact, including millions in fraud savings, 40% faster analysis, and reusable platform tooling that accelerated multiple teams.”

“AI/ML engineer with hands-on experience building a healthcare-focused generative AI application end-to-end, from architecture and data design through deployment, monitoring, and iterative improvement. Particularly strong in multi-agent LLM systems, fine-tuning, and safety guardrails, with measurable impact including a 20% accuracy lift to 91% and 10% latency improvement in a nutrition recommendation pipeline.”

Sai Prudhivi

Screened

Mid Software Engineer specializing in full-stack microservices and cloud platforms

New York, NY4y exp

“Backend-focused engineer with experience building high-volume policy management and enterprise pricing systems, including Django/FastAPI/Flask services, Kafka-based async workflows, and Prometheus/Grafana observability. While they have not yet shipped a customer-facing AI agent or production LLM integration, they bring strong cloud, API, reliability, and scalable system design fundamentals that translate well to responsible AI infrastructure work.”

“Built and launched a production AI knowledge assistant at Virtusa used by 8,000 people, combining RAG, tool use, and strong reliability practices to cut lookup time by 60%. Also owns full-stack delivery, including a real-time transaction monitoring dashboard built with React, Spring Boot, and Kafka handling 200K API requests per day.”

“Backend/platform engineer with strong ownership of production systems, including a full Azure migration from a VM-based monolith to a containerized, event-driven microservices architecture. They combine cloud infrastructure, LLM/RAG optimization, and pragmatic stakeholder management, with measurable wins including 90% infra cost reduction, faster deployments, and significantly improved latency and token efficiency.”

Sri Mounika Jammalamadaka

Screened

Mid-level AI/ML Engineer specializing in GenAI, LLMs, and data platforms

Fairfax, VA6y exp

“Built and helped deploy a production RAG-based LLM assistant for HVAC anomaly diagnostics, partnering closely with field engineers and operations teams to make AI outputs trustworthy in real workflows. Stands out for practical post-launch optimization work—improving retrieval quality, reducing hallucinations, and stabilizing non-deterministic behavior—which contributed to roughly a 40% reduction in diagnosis time.”

“Software engineer currently building for a wealth management platform, with hands-on ownership across React/TypeScript front ends, Azure serverless backends, and SQL data systems. Stands out for turning hackweek concepts into production features, building observability infrastructure from scratch, and delivering measurable gains in latency, uptime, incident response, and client satisfaction.”

“New grad candidate with graduate research experience building a multi-agent RAG pipeline from scratch, including worker-coach orchestration and an evaluation framework. Most notably, they improved structural similarity from 67% to 98% by designing validation and retry logic to reduce hallucinations, showing strong practical depth in agentic AI systems.”

“Full-stack software engineer who shipped production systems in academic and e-commerce contexts, including a UC Irvine course recommendation platform with async Kafka-based OCR processing (Tesseract) and LangChain-driven recommendations. Strong in building polished React/TypeScript dashboards (Figma-to-implementation) and owning Python backends (FastAPI/Flask) with solid API design, caching, and AWS EKS deployments; delivered measurable impact (tripled engagement, ~50% faster processing).”

“Full-stack engineer who built an early-stage social platform for scriptwriters (Scriptscape) from scratch, owning everything from React Native/React UX to Node/Postgres APIs and AWS deployment. Demonstrates strong production-minded engineering with CI/CD, observability, and scalability patterns (cursor pagination, indexing, background jobs), plus experience hardening flaky third-party integrations with idempotency and backfills.”

“Backend engineer with fintech/AI startup experience who built an Azure serverless, event-driven pipeline for large-scale crypto sentiment analysis and semantic search (OCR/NLP to vector search) and integrated LLM + blockchain data for predictive insights. Demonstrated measurable impact (25% lower retrieval latency, 10% fewer data errors, 15% higher engagement) and has led safe microservices migrations with strong security and reliability practices.”

“AI Engineer at Humanitarian AI who has built and productionized both a LangGraph-based multi-agent workflow system and a RAG pipeline (OpenAI embeddings + vector DB) with rigorous evaluation/guardrails. Reports strong measurable impact (60% faster workflow delivery, 40% fewer incidents, 70% reduced research time) and has prior enterprise modernization experience at Infosys migrating ETL to microservices with zero production incidents.”

“Backend/data engineer with strong production experience across Python microservices (FastAPI) and AWS serverless/data platforms (Lambda, API Gateway, Glue, Redshift). Demonstrates reliability and incident ownership (rate limits, retries/circuit breakers, monitoring) and has delivered measurable SQL performance gains (12–15s to <800ms, ~60% CPU reduction). Seeking fully remote work and not open to relocation/onsite meetings.”

“Backend engineer with deep experience modernizing a provider credentialing/compliance platform with multiple upstream/downstream integrations. Focused on building predictable, scalable REST APIs (primarily ASP.NET Core; framework-agnostic approach applicable to FastAPI), improving performance via DB/query optimization, and hardening workflows with idempotency, transactions, feature flags, and strong auth/RBAC patterns.”

“Built and deployed an agentic RAG platform at Centene Health to support healthcare claims and complaints workflows (Q&A for claims agents, executive complaint summarization, and compliance triage/classification). Experienced in LangChain/LangGraph orchestration, production deployment on AWS with FastAPI/Docker/Kubernetes, and implementing HIPAA-compliant guardrails to reduce hallucinations and ensure explainable outputs.”

“Developed and deployed CaptionFace, a production vision-language system that boosts low-resolution/surveillance face recognition by generating discriminative natural-language captions (ViT encoder + GPT-2 decoder) and enabling text-to-face retrieval and zero-shot recognition. Orchestrated distributed training on Kubernetes with MLflow tracking, mixed-precision optimization, and comprehensive evaluation including out-of-domain robustness; collaborated with non-technical NSF project stakeholders via demos, visualization, and clear documentation.”

“Built and shipped a production GPT-4 + RAG customer support chatbot that materially improved support operations (response time 4 hours to <3 minutes; ~65% tier-1 ticket automation). Demonstrates strong end-to-end LLM engineering across retrieval (Sentence Transformers/Pinecone), safety (multi-layer moderation), cost/latency optimization (caching/streaming, Celery/Redis), and rigorous evaluation/monitoring (shadow deploys, Datadog, 500+ test cases), plus proven stakeholder buy-in leading to 80% adoption.”