Vetted Apache Kafka Professionals

“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 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.”

“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(strict schemas, confidence thresholds, monitoring, and manual-review fallbacks) and runs evaluation loops with labeled historical documents to drive prompt/validation improvements and reduce manual review.”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 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 (Marsh McLennan) who evolved a high-volume claims automation pipeline in Python, emphasizing thin APIs with background job processing, strong validation/retries, and production-grade observability. Experienced in secure FastAPI API design (centralized JWT/RBAC), multi-tenant Postgres/Supabase-style row-level security, and low-risk refactors using parallel runs and feature flags; targeting founding-engineer scope roles.”

“Data/analytics engineer who owned Angi’s service-request funnel event pipeline end-to-end, routing events server-side to bypass ad blockers and recovering ~15% lost tracking at millions of events/day. Built Snowflake/dbt reporting tables powering Looker dashboards, with strong emphasis on validation, monitoring/alerting, and safe schema evolution. Also shipped a reusable flow state management backend service with TTL storage, CI/CD, and developer-friendly APIs.”

“Data engineer focused on reliable, production-grade pipelines and data services: has owned end-to-end ingestion-to-serving workflows processing millions of records/day, using Airflow, Python/SQL, and PySpark. Demonstrates strong operational rigor (monitoring, retries, idempotency, backfills) and measurable outcomes (98% stability, ~30% faster processing), plus experience exposing curated warehouse data via versioned REST APIs.”

“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”

Siddhanth Erramaraju

Screened

Junior Full-Stack Developer specializing in MERN and AWS

Birmingham, AL3y exp

“Backend engineer focused on Python/Flask APIs and cloud-native delivery: builds stateless services with JWT auth, validation, and scalable deployment on Kubernetes using a GitOps workflow (ArgoCD-style) with easy rollbacks. Has also implemented Kafka-based real-time event pipelines and supported phased hybrid cloud/on-prem migrations with parallel runs and controlled cutovers.”

Chandan Chalumuri

Screened

Mid-level Data Scientist specializing in ML, NLP, and Generative AI

Tempe, AZ4y exp

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

Apurva Banka

Screened

Mid-level Full-Stack & AI Engineer specializing in cloud, data platforms, and LLM automation

Houston, TX5y exp

“Software engineer/product builder who has owned an agentic affiliate lead-gen platform end-to-end (Django + React/TypeScript) and deployed it on Kubernetes in anticipation of 10x user growth from ~5K DAUs. Also has healthcare claims microservices experience using Kafka, including hands-on performance tuning to address consumer lag and broker pressure, and built an internal downtime alerting tool adopted across the organization.”

RAJ KUMAR

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices

Chicago, IL6y exp

“Backend engineer at Discover who built and scaled Python/Flask services for a card dispute resolution platform, tackling long-running external network validations with Celery+Redis and delivering measurable gains (response time ~3s to <300ms; throughput +40%). Experienced in high-scale PostgreSQL/SQLAlchemy optimization (partitioning, read replicas, N+1 avoidance), event-driven systems with Kafka, and integrating ML fraud detection using AWS SageMaker/Lambda/ECS with clear separation of real-time vs batch processing.”

Vedang Jadhav

Screened

Mid-Level Software Engineer specializing in cloud-native microservices on AWS

New York City, NY5y exp

“Backend engineer with experience across healthcare and fintech platforms (Anthem, Citia) building high-throughput Python microservices with strong compliance/security focus (HIPAA, tenant isolation). Has integrated ML workflows into production systems (ResNet embedding-based image similarity) using async pipelines (Celery/Redis) and AWS (Lambda/S3/ECS), delivering measurable performance and fraud/content-integrity improvements at scale.”

vineetha Pulipati

Screened

Mid-level Software Engineer specializing in backend microservices and cloud data pipelines

MO, USA4y exp

“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.”

Sharanya Rao

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare

Remote, USA3y exp

“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.”

Mike Gardiner

Screened

Technology Executive / Engineering Director specializing in AI-driven platform transformation

Lehi, UT12y exp

“Built a 0-to-1 iOS mobile gardening application that helps users plan, track, and harvest crops with pest control guidance, weather, and climate-zone-based planting date recommendations. Demonstrated strong customer discovery and MVP-first product execution, including a major data challenge: compiling US climate zone data for every ZIP code from widely dispersed public sources into an app-ready database.”

Abhishek Saraswat

Screened

Mid-level Software Engineer specializing in Java backend microservices

Arlington, TX4y exp

“Backend/distributed-systems engineer focused on automation and near-real-time processing, building Java/Spring Boot microservices with Kafka, PostgreSQL, and AWS. Strong in scaling and reliability work—debugging tricky asynchronous messaging issues (delays, duplicates, out-of-order events) and improving resilience/observability with retries, fallbacks, logging, and monitoring. No production ROS/ROS2 experience yet, but has studied core ROS concepts and draws clear parallels to event-driven architectures.”

Madhu Sriram Sengottu Velan

Screened

Entry-Level Software Engineer specializing in backend systems and distributed services

Chennai, IN2y exp

“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.”

Aishwarya Yenumula

Screened

Mid-level Full-Stack Java Engineer specializing in microservices, React, and Azure

Austin, TX5y exp

“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.”

Venkat Palaparthi

Screened

Senior Software Engineer specializing in cloud-native microservices and secure enterprise platforms

Dallas, TX6y exp

“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).”

srilekha pothula

Screened

Mid-level Data Engineer specializing in cloud data pipelines for healthcare and financial services

Bloomfield, CT4y exp

“Data engineer with ~4 years of experience (Cigna) building and operating Azure Data Factory pipelines for healthcare claims/member/provider data at 2–3M records/day. Emphasizes reliability and downstream safety via schema/data-quality validation, quarantine workflows, idempotent processing, and backfills; also improved runtime ~20% through SQL optimization and served curated datasets through versioned views and well-documented, analyst-friendly interfaces.”