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Vetted Apache Kafka Professionals
Pre-screened and vetted.
Abhinay Mangasamudram
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices
“Backend engineer with cloud-native Python/Flask experience building high-throughput financial platforms (loan origination intelligent document processing and real-time fraud detection). Has scaled microservices on AKS with event-driven Azure messaging, delivered measurable performance gains (e.g., 700ms→180ms query latency; ~40% API improvements), and implemented strong security controls (OAuth2/JWT, Azure AD RBAC, audit logging, AES-256/TLS) for sensitive regulated data.”
Shireesh Kumar Poral Ashok Kumar
Senior Full-Stack Developer specializing in cloud-native web applications
“Full-stack engineer who built an oil & gas analytics dashboard backend using FastAPI, MongoDB, and Redis with a metadata-driven design for dynamic plotting. Shipped an LLM-powered chatbot (LangChain + tool/function calling) to let engineers query analytics in natural language, and also built a multi-step university chatbot workflow with Azure logging, confidence scoring, and human-in-the-loop review.”
Indraneel V
Mid-level Cloud & DevOps Engineer specializing in AWS/Azure, Kubernetes, Terraform, and CI/CD
“IBM Power/AIX infrastructure engineer with hands-on production experience across Power8/Power9 frames, VIOS and HMC, including resolving a production LPAR outage caused by vFC mapping issues. Has operated PowerHA clusters for critical finance workloads, running quarterly failover tests and handling an unplanned failover triggered by a network adapter failure, then improving resilience with redundancy and monitoring automation.”
ManiKumar Chintha
Mid-level Full-Stack Java Developer specializing in microservices and cloud (AWS/Azure)
“Backend/full-stack Java engineer at PNC Bank specializing in real-time fraud detection systems. Built event-driven Spring Boot + Kafka microservices with PostgreSQL/Redis performance tuning, and shipped a production LLM-powered RAG feature for fraud analysts with strong guardrails (grounded internal data, structured prompts with references, human-in-the-loop) plus an evaluation loop using labeled historical fraud cases.”
Swarnali Biswal
Senior DevOps/Platform Engineer specializing in Kubernetes and cloud infrastructure
“DevOps/Infrastructure engineer with hands-on production experience building Jenkins CI/CD pipelines that provision Kubernetes infrastructure and process data into a MapR cluster. Uses Terraform to provision AWS resources (EC2, S3, VPC, subnets) with remote state in S3, separate environment state files, and code review/validation practices; targeting $135k base.”
Filmon Tesfay
Senior Full-Stack Developer specializing in cloud-native FinTech and AI platforms
“Full-stack engineer with strong production ownership: built and operated a real-time transaction monitoring/fraud-alerting system using Java Spring Boot, Kafka, Docker, and AWS with CI/CD. Demonstrates metrics-driven operations (latency, stability, consumer lag, true/false positives) and reliability patterns for integrations (idempotency, retries/backoff, DLQs, reconciliation/backfills), plus modern React/TypeScript + Node/Postgres architecture experience.”
Santhoshi Priya Sunchu
Mid-level Data Scientist specializing in NLP and predictive modeling
“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”
Valeriy Filin
Senior Backend/Cloud Engineer specializing in IaC, SaaS platforms, and ML/Computer Vision
“Backend/infrastructure engineer with experience across API development (FastAPI/MySQL/SQLAlchemy), Kubernetes deployments, and large-scale data processing—built a Dockerized Python pipeline to pre-aggregate ~1B Graylog events for efficient querying. Has enterprise infrastructure automation background at Hewlett Packard Enterprise (Datafabric) using Terraform/Ansible with fail-fast and rollback practices, plus Kafka-based sensor streaming prototypes to Google Cloud with Java workers and autoscaling.”
Rahul Solleti
Mid-Level Software Engineer specializing in cloud-native microservices and full-stack web apps
“Backend/platform engineer focused on real-time financial fraud detection and transaction monitoring, building low-latency FastAPI + Kafka systems with strong reliability patterns (DLQs, idempotency) and cloud observability. Has hands-on Kubernetes delivery across AWS EKS and Azure AKS with automated CI/CD and GitOps-style deployments, plus experience migrating legacy C# / Java monoliths to containerized microservices using Terraform/ARM and zero-downtime rollout strategies.”
Sanjana Akula
Senior Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
“Front-end engineer with experience at Optum and Wells Fargo maintaining internal React/Angular component libraries and design-system-aligned UI modules used across multiple apps. Known for stabilizing shared libraries via semantic versioning, Jest test automation, and high-quality documentation, plus measurable performance wins (≈40% faster dashboard loads) through profiling-driven React and API optimizations.”
Sivapriya Rachakonda
Mid-Level Software Engineer specializing in cloud-native microservices on AWS and Kubernetes
“Backend engineer who built a stateless Python/Flask service supporting a healthcare-document ETL pipeline, offloading heavy processing to Celery workers and adding strong observability (metrics, structured logs, audits). Demonstrates practical performance/reliability work: batch chunking, priority queues, autoscaling by queue depth/CPU, DLQ routing, and PostgreSQL tuning (indexes, pagination) to cut slow API responses. Also has experience deploying real-time ML classification via TensorFlow Serving behind a FastAPI wrapper and integrating models via REST/gRPC.”
Ashutosh Meena
Junior Embedded Software Engineer specializing in IoT and microcontrollers
“Embedded/software engineer with hands-on Raspberry Pi work building a WhatsApp-controlled camera/servo system using TCP/IP plus Selenium automation of WhatsApp Web. Brings production DevOps experience from Infosys (Docker/Kubernetes, CI/CD, microservices, Kafka) and a methodical hardware/software debugging workflow using lab tools like oscilloscopes and multimeters.”
Tyler Stroud
Executive Engineering Leader specializing in AdTech and scalable cloud platforms
“Engineering leader with experience in small, bootstrapped startups and exposure to VC environments, currently pursuing CTO-level opportunities. Thrives in fast-iterating, high-uncertainty settings and emphasizes data-driven clarity plus strong problem/market validation when evaluating new ventures.”
Shital Agarwal
Senior Software Engineer specializing in backend, microservices, and full-stack web development
“Software engineer who delivered a dynamic service-fee system for a Belgian online grocery e-commerce platform by carving out a Spring Boot microservice from a monolith, integrating Google distance APIs, Redis caching, and CI/CD for production rollout. Also built an OpenAI-powered university chatbot with agent/workflow orchestration during academia, emphasizing availability and fallback behavior.”
Butchi Venkatesh Adari
Mid-level Machine Learning Engineer specializing in LLM platforms and robotic perception
“Built and shipped a production multi-agent personal financial assistant at AlphevaAI on AWS ECS, combining FastAPI microservices, Redis/SQS orchestration, and Pinecone-based hybrid RAG (semantic + BM25) to ground financial guidance. Improved routing accuracy with an embedding-based SetFit + logistic regression intent classifier feeding an LLM router, and optimized UX with live streaming plus cost controls via model tiering and caching.”
Shabari Vignesh
Mid-level Data Engineer specializing in cloud data platforms and AI agents
“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”
Abhiroopsudansh Karengula
Mid-level AI/ML Engineer specializing in production ML, RAG systems, and MLOps
“Built and shipped a widely adopted, production-grade RAG internal search assistant that unified scattered engineering knowledge, deployed as a FastAPI service on Kubernetes with FAISS + LangChain. Demonstrates deep practical expertise in retrieval tuning (chunking, hybrid search, re-ranking) and in making LLM workflows reliable in production via guardrails, monitoring, and evaluation, plus strong cross-functional delivery with non-technical operations teams.”
Ishika Khokhani
Mid-level Software Engineer specializing in AI systems and distributed platforms
“Built OpenGPU features spanning React/TypeScript, Go orchestration, PostgreSQL, Redis, and Stripe, with a strong focus on reliability, transaction integrity, and low-latency distributed systems. Also shipped LLM product infrastructure, including persona-conditioned frameworks and reusable prompt/model abstractions, showing a blend of systems engineering and fast product iteration.”
Keerthi Basam
Mid-level Software Engineer specializing in AI/ML for FinTech and Healthcare
“Built and deployed an end-to-end fintech product, FinSight, for bank statement analysis and financial Q&A using a production-style RAG architecture. Stands out for combining FastAPI, OpenAI embeddings, FAISS, hybrid SQL/vector retrieval, and practical reliability work like chunking optimization, validation, and low-latency performance tuning.”
Tarun Madimi
Mid-level Full-Stack Developer specializing in cloud-native enterprise platforms
“Software engineer focused on backend and full-stack development who is already integrating AI deeply into day-to-day engineering workflows. Stands out for experimenting with multi-agent setups where separate agents handle planning, coding, review, testing, and documentation, while maintaining strong human oversight around quality, security, and performance.”
Naveen K
Mid-level Full-Stack Software Engineer specializing in AI-powered backend systems
“Full-stack engineer with hands-on ownership of a real-time analytics and alerting dashboard built with React/TypeScript, Node.js, Kafka, Redis, and PostgreSQL. Also contributed to an internal LLM-powered support automation system, focusing on backend orchestration, RAG-based reliability, and Kubernetes deployment. Stands out for combining product-minded zero-to-one execution with strong distributed systems and AI integration experience.”
Vasavi Nagavalli Gollapalli
Senior Full-Stack Software Engineer specializing in backend systems and cloud-native APIs
“Full-stack engineer with startup-style ownership across backend, frontend, and AI systems, spanning Java/Spring, React, Node/TypeScript, and LLM-powered retrieval. Shipped a workspace intelligence layer using LangChain, OpenAI, and Pinecone to paying customers, while also improving core product metrics like workspace creation success (+30%), latency (450ms to 280ms), and deployment cycle time (-40%).”
Jaisurya Kolluru
Mid-level Software Engineer specializing in cloud-native backend and AI systems
“Full-stack AI engineer with recent CVS Health experience building production healthcare products that combine Spring Boot, React/TypeScript, Kafka, AWS, Kubernetes, and OpenAI/LangChain. Particularly strong in turning generative AI and RAG-based clinical note summarization into scalable, provider-friendly workflows with real-time patient insights and production monitoring.”
Sai Addala
Mid-level AI/ML Engineer specializing in financial risk, fraud analytics, and forecasting
“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”