Vetted Apache Kafka Professionals

“Backend engineer/technical lead with experience building and operating real-time blockchain analytics systems at Merkle Science. Owned high-traffic Django/DRF services and Kafka streaming pipelines processing millions of events daily, with deep focus on performance (N+1 fixes, indexing, caching) and reliability (DLQs, retries, monitoring). Also led containerization and Kubernetes/GitOps-style CI/CD on Google Cloud, including a migration off Google App Engine to reduce cost and improve scalability.”

“Backend/platform engineer who owns policy-lifecycle workflow microservices built in Python/FastAPI with async + DDD, Kafka event processing, SQLAlchemy, JWT/RBAC, and Redis caching (cut DB load ~40%). Experienced deploying Java and Python microservices to Kubernetes with Helm and GitOps (ArgoCD) plus Jenkins/GitHub Actions pipelines to AWS/ECR, and has supported phased on-prem-to-cloud migrations with dependency mapping and data consistency strategies.”

“Backend engineer focused on cloud analytics/cost-optimization systems, with strong AWS-centric architecture experience (serverless Lambda, ECS, CloudWatch). Demonstrated deep PostgreSQL/SQLAlchemy optimization at million-record scale (including JSONB/GIN indexing) and built production ML inference services with S3-based model versioning and Airflow retraining pipelines. Also has hands-on multi-tenant SaaS design (separate schemas + RLS) and high-throughput background processing using Celery/Redis with measurable performance gains.”

“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”

“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”

“Backend engineer with Morgan Stanley experience building and owning an end-to-end Python FastAPI microservice for high-volume market data used by trading and risk systems. Strong in performance tuning and reliability (PySpark, Redis caching, async APIs), real-time streaming with Kafka, and production operations (Docker/Kubernetes, GitOps-style CI/CD, monitoring). Has led cloud/on-prem migration work across AWS and Azure, including fixing Azure Synapse performance issues via query and pipeline redesign.”

“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”

“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”

“Robotics engineer with hands-on experience scaling a multi-vendor heterogeneous warehouse robot fleet, building a distributed “traffic manager” for collision avoidance and real-time rerouting using CBS/MAPF and DCOP-style negotiation. Strong real-time/safety-critical systems background (RTOS, deterministic lock-free multithreading) plus modern perception and simulation tooling (CNN-LSTM/transformers, CARLA/Isaac Sim, VIO/GTSAM, camera-IMU calibration). Startup-oriented and comfortable moving quickly from prototype to production.”

“Hands-on backend/AI engineer who solo-built two production Claude-based agent systems: an internal Slack RAG over Confluence/Jira/code/regulatory docs and a HIPAA/GDPR-compliant patient chatbot with embedding guardrails and expert-in-the-loop evals. Also architected a multi-region patient portal + microservices platform with Terraform/CI-CD and federated gateways, delivering major onboarding automation and strong reliability wins (PgBouncer, chaos/perf testing).”

“Backend/AI engineer from an early-stage Japan-based startup (WorkAI) who built a multi-tenant RAG system integrating Notion/Slack/Google Drive with Pinecone and OpenAI, including a chatbot retrieval workflow. Experienced in production reliability (rate limits, retries, verification layers), strong Python/FastAPI engineering practices, and PostgreSQL performance optimization; currently based in India and needs sponsorship.”

“Backend engineer with American Express experience maintaining an internal Python/Flask rewards simulation microservice used by product analysts and QA. Demonstrated strong performance and scalability work: moved batch simulations to Celery, added Redis caching to cut DynamoDB latency, and tuned Postgres/SQLAlchemy queries with EXPLAIN ANALYZE and composite indexes (bringing API responses under ~200ms by queueing jobs). Also has experience integrating ML via Flask-based model-serving APIs (scikit-learn/LightGBM packaged with joblib) and designing multi-tenant data isolation and tenant-specific configuration systems.”

“Full-stack engineer with hands-on ownership of a real-time loyalty rewards notification system at Dell, spanning React UI, Spring Boot/Node microservices, Kafka event processing, and Oracle/Postgres persistence. Strong production operations experience across AKS/Azure DevOps and AWS (EC2/RDS/S3, autoscaling, CloudWatch), including resolving peak-load Kafka lag and API latency incidents through scaling and performance tuning.”

“Backend engineer working on high-volume insurance claims intake systems who shipped a production GenAI document-classification capability in Spring Boot microservices. Emphasizes reliability in LLM systems (strict schemas, confidence thresholds, monitoring, and manual-review fallbacks) and runs evaluation loops with labeled historical documents to drive prompt/validation improvements and reduce manual review.”

“Full-stack engineer with strong production ownership in banking/identity & entitlements systems, building Spring Boot + Postgres/Redis services and React dashboards, then deploying on AWS EKS with Jenkins CI/CD. Demonstrated impact through reduced authorization latency and fewer access-related support tickets, plus strong observability and reliability practices (CloudWatch, tracing, autoscaling, Kafka pipelines with DLQs and reconciliation).”

“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”

“Full-stack engineer who built a cloud storage app feature (file upload/management) with Next.js App Router + TypeScript and owned post-launch improvements. Also has internship experience building a geospatial AI chatbot: designed Postgres/PostGIS data models and optimized spatial queries, and implemented an LLM workflow orchestrated with LangChain/LangGraph plus a RAG pipeline grounded in OpenStreetMap data to reduce hallucinations.”

“Data engineer focused on AWS-based enterprise data platforms, owning end-to-end pipelines from multi-source batch/stream ingestion (Glue/Kinesis/StreamSets/Airflow) through PySpark transformations into curated datasets for Redshift/Snowflake. Emphasizes production reliability with strong monitoring/observability and data quality gates, and reports ~30% performance improvement plus improved SLAs and latency after optimization.”

“Backend engineer with healthcare domain experience building AI-driven radiology workflow systems. Evolved tightly coupled APIs into secure, reliable FastAPI-based services by moving heavy imaging/data processing into idempotent asynchronous pipelines with retries, feature-flagged incremental rollout, and strong data-integrity controls (constraints, backfills, validation). Strong focus on defense-in-depth security for sensitive patient data (OAuth2/JWT, RBAC, and database-level protections).”

“Backend engineer who built and launched a warehouse locations/inventory microservice for Walmart, supporting a new product rollout with on-call war-room ownership and now running across all US distribution centers. Emphasizes reliability and correctness (background syncs, 2PC concepts, alerting) plus design-first API development in Python/FastAPI with OAuth/JWT and RBAC, and has led staged legacy-to-microservice migrations with continuous data integrity verification.”

“Backend engineer with hands-on experience modernizing high-volume transactional systems by decomposing monoliths into Spring Boot microservices on AWS, using Kafka for async workflows and Redis/SQL tuning for latency. Has built Python/FastAPI services with strong API contracts and production-grade security (OAuth2/JWT, RBAC, row-level security), and proactively hardened payment flows against race conditions and double-charging via idempotency.”

“Backend/data platform engineer who has built and operated a cloud-native media ingestion/processing platform in Python (Django/DRF, FastAPI) with Kafka, Postgres, and Redis, emphasizing multi-tenant security and reliability. Delivered AWS production systems combining EKS and Lambda with Terraform + GitHub Actions/Helm, and built Glue-based ETL pipelines with strong schema-evolution and data-quality practices; also modernized SAS analytics into Python on AWS. Seeking fully remote roles with a $120K–$140K base range.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”