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Vetted Apache Kafka Professionals
Pre-screened and vetted.
Srinivas Matta
Mid-Level Full-Stack Software Developer specializing in cloud-native web platforms
“Software engineer at Capital One who owned and shipped AI-driven personalization and internal insights dashboards end-to-end, emphasizing fast iteration with feature flags and tight user feedback loops. Built a TypeScript/React + Spring Boot/Python document automation platform with compute-heavy NLP microservices, async workflows, and production-scale reliability/performance practices (Kafka/RabbitMQ-style queues, Redis caching, tracing).”
SAI RAKAM
Mid-Level Software Engineer specializing in backend microservices and FinTech payments
“Capital One engineer focused on fraud and payments platforms, owning end-to-end services and internal tools used by fraud analysts. Built high-traffic Kafka/REST systems and real-time React/TypeScript dashboards (WebSockets, Redis), with strong emphasis on observability, idempotency, and scalable microservices. Successfully drove adoption of AI-assisted fraud classification by pairing transparency and manual overrides with measurable workflow improvements.”
Sayali Patil
Mid-level Python Full-Stack Developer specializing in Healthcare and FinTech
“Backend engineer with hands-on experience building a fraud-transaction monitoring system in Python/Flask, architected as Dockerized microservices and integrated with Kafka for high-volume streaming. Demonstrates strong performance and reliability chops across PostgreSQL/SQLAlchemy tuning (EXPLAIN ANALYZE, N+1 fixes, bulk ops), multi-tenant data isolation, and scaling via background workers + Redis caching, plus real-time ML inference deployment using TensorFlow on AWS.”
Vaibhav Sharma
Mid-level Software Engineer specializing in AI/ML and data platforms
“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”
Saksham Khatwani
Mid-level Software Engineer specializing in NLP and search systems
“Built an AI journaling app at HackCU 2025 featuring a speaking AI avatar with long-term memory via RAG (ChromaDB) and low-latency microservices coordinated through Kafka, including deployment under AMD/non-CUDA constraints using a quantized Llama 8B model. Also has Goldman Sachs experience deploying a Trade UI on Kubernetes with CI/CD rollback automation, plus a healthcare AI internship at CU Anschutz collaborating closely with physicians on diagnostic reasoning and dataset annotation.”
Prasannakumar B Vardi
Senior Software Engineer specializing in low-latency ad targeting and distributed backend systems
“Backend/platform engineer who built a high-scale audience segmentation and real-time targeting system using Spark/Glue + S3/Hudi and low-latency API services backed by Redis/relational stores. Demonstrates strong production rigor: Spark performance tuning to eliminate OOM failures, API idempotency/caching to cut p95 latency ~40%, and careful dual-run/feature-flag migrations with reconciliation and rollback runbooks. Experienced implementing layered security with JWT/OAuth, RBAC/ABAC, and database row-level security to prevent privilege escalation.”
Kanaka Chalam Volety
Staff DevOps/SRE Engineer specializing in AWS, Kubernetes, and GitOps
“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”
Sankalp Tiwari
Mid-Level Software Engineer specializing in backend microservices and FinTech data pipelines
“Backend engineer at Goldman Sachs who built LLM-powered reconciliation/reporting services and high-throughput Kafka pipelines (8M+ events/day). Strong in production-grade Python/FastAPI microservices on Kubernetes with GitOps-style CI/CD, plus experience migrating legacy reporting/settlement services onto an internal Kubernetes platform using shadow deployments and gradual cutovers.”
Akshit Modi
Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps
“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”
Aditya Jaiswal
Intern Software Engineer specializing in cloud, DevOps, and applied AI
“Full-stack engineer with startup ownership experience (Aiir) building 15+ TypeScript/Go microservice APIs on GCP Cloud Run with Kafka-based async event streaming and React CRM integrations for billing/analytics. Strong post-launch operator who tuned Oracle performance (partitioning/indexing/query optimization) and validated a 23% retrieval-time reduction via AWR, and has a quality/DevSecOps mindset (94% Pytest coverage, GitHub Actions, SonarQube, Twistlock, CloudWatch) including migrating 18+ production CI/CD pipelines.”
Bhavyasree Chinthala
Mid-level Data Engineer specializing in cloud data pipelines and real-time streaming
“Data engineer with PNC Bank experience owning high-volume financial transaction pipelines end-to-end (Kafka/REST ingestion through Spark/Glue transformations to Redshift serving) for risk and fraud analytics. Built strong reliability and data quality practices (Great Expectations, reconciliation, Airflow alerting, idempotent retries, incremental/windowed processing), reporting 40% ingestion efficiency gains and ~99.9% data accuracy.”
Suloni Praveen
Entry-Level Software Engineer specializing in data engineering and ML systems
“Built an end-to-end Next.js/TypeScript LLM-based scientific PDF analyzer using local Ollama/Llama inference to prioritize privacy and cost, producing structured research artifacts (e.g., authors/methods/findings) with ~92% extraction accuracy. At Qualtrics, helped replace a batch pipeline with a real-time, low-latency ML inference service (Python/Go on Kubernetes) using Redis caching, Grafana-based observability, and graceful fallbacks to protect UX during failures.”
Josiah Aviles
Senior AI/ML Software Engineer specializing in Generative AI and RAG systems
“Built and owned Alight's AI-powered Search Summary feature end-to-end, using a RAG pipeline with OpenSearch and Bedrock, and drove a 20% increase in user feedback scores. Stands out for bringing rigorous production evaluation to LLM systems via live LLM-as-a-judge monitoring, and for experience with advanced agentic architectures, hybrid search, and reranking at scale.”
Yinghai Yu
Mid-level Data Engineer specializing in cloud data platforms and AI/ML pipelines
“Data-engineering-oriented candidate with hands-on experience building an agentic AI product and operational automation workflows. They described automating inventory-to-ERP discrepancy reconciliation with anomaly detection and daily reporting, and also have practical scraping/automation experience dealing with Cloudflare-protected sites using Selenium and Puppeteer.”
Shruti Pangare
Junior AI/ML Software Engineer specializing in LLMs and data-intensive systems
“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”
Prathyusha Mardhi
Mid-level AI/ML Engineer specializing in LLM agents and workflow automation
“AI/LLM engineer with strong healthcare domain depth who has shipped production-grade agents for care coordination and clinical workflow automation. Stands out for combining Knowledge Graph RAG, LangGraph orchestration, and rigorous eval/guardrail systems to improve reliability in high-stakes environments, with measurable gains in review time, hallucination reduction, latency, and clinician adoption.”
Mounya Bonuga
Mid-level AI/ML Engineer specializing in multimodal AI and recommendation systems
“ML/AI engineer with hands-on ownership of a production LLM/RAG system at Goldman Sachs, focused on workflow automation and large-scale document search for operational teams. They combine strong MLOps and backend engineering skills with practical GenAI evaluation and safety practices, and cite measurable impact including 22% better task guidance accuracy and sub-second search across millions of records.”
An Duong
Senior Full-Stack Engineer specializing in SaaS, mobile, and AI platforms
“Product-minded full-stack engineer with experience shipping engagement features and core communication systems at DribbleUp and Expys. Stands out for combining rapid MVP execution with rigorous iteration: delivered a leaderboard feature that lifted engagement by 8% initially and 20% overall, built a chat MVP in 3 days, and has hands-on experience deploying LangChain-based concierge agents with evals and human review.”
Keerthi Pericherla
Mid-level Full-Stack Developer specializing in cloud-native FinTech applications
“Frontend engineer with HCL Tech experience building loan operations dashboards and React/TypeScript data-heavy interfaces. Stands out for combining maintainable component architecture with hands-on performance tuning, including a reported 30% load-time improvement in a production visualization-heavy application.”
“Frontend-focused product engineer with hands-on experience designing and shipping real-time dashboards and alerting systems in regulated domains like banking and healthcare. Has led both UX/design and implementation work, combining React/TypeScript and Angular frontend expertise with Kafka-driven event architectures, performance optimization, and production monitoring.”
Achal Shah
Mid-level Full-Stack Software Developer specializing in scalable web applications
“Frontend-focused product engineer with experience at IBM and Intel building internal enterprise platforms, especially incentive and employee operations workflows. They repeatedly describe owning both UX/design decisions and the React frontend implementation, with strong emphasis on reusable components, API integration, and shipping ambiguous workflow products into production.”
Uma mahesh
Mid-level AI Software Engineer specializing in Generative AI and healthcare automation
“Backend/AI engineer with 5+ years spanning ServiceNow, Cognizant, and GE Healthcare, where they built agentic AI and RAG systems for safety-critical medical device workflows. Particularly compelling for teams needing production LLM systems in regulated environments: they combine cloud deployment and evaluation rigor with HIPAA-aware privacy controls, explainability, and measurable business impact.”
Ziyu Wang
Mid-level Full-Stack Engineer specializing in AI platforms and agents
“Front-end engineer focused on high-performance real-time browser applications, with hands-on ownership of an AI-integrated education platform and interactive admin tooling. Stands out for deep browser fundamentals knowledge and measurable UI performance gains, including a 40% reduction in client-side response latency.”
Ning Ding
Entry-level Software Engineer specializing in AI and full-stack applications
“Full-stack engineer who described a sophisticated forum platform spanning Next.js, NestJS, MongoDB, MySQL, PostgreSQL, RabbitMQ, and AWS. Stands out for strong systems thinking: they designed for type safety, eventual consistency, idempotent async moderation, analytics scalability, and production reliability, while also driving an architectural refactor that reduced new post-type delivery from multi-day work to about half a day.”