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Vetted Apache Kafka Professionals
Pre-screened and vetted.
Vineeth Reddy Vallapureddy
Mid-level Full-Stack Software Engineer specializing in backend microservices and enterprise AI tools
“Backend/platform engineer with experience across C3.ai (supply chain demand planning) and Amdocs (telecom), working on large-scale data systems and microservices. Has driven first-time adoption experiments of Snowflake + Spark to handle billion-record workloads, built Jenkins-to-Kubernetes delivery pipelines with Nexus artifact management, and implemented Kafka streaming between microservices with HA and retry/error-handling patterns.”
Pooja Dokuri
Mid-level AI/ML Engineer specializing in GenAI, RAG pipelines, and cloud MLOps
“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”
Divyam Agrawal
Mid-level Machine Learning Engineer specializing in LLMs and NLP classification systems
“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”
Evan Z
Junior Software Engineer specializing in video streaming and processing systems
“Software engineering intern at China Telecom who built and continuously evolved a real-time transaction platform ("Smart Tangerine") focused on strong consistency and peak-hour concurrency. Implemented microservices with Redis and RabbitMQ to decouple heavy processing and cut latency (~80ms to ~30ms), and led a zero-downtime migration from a monolith using strangler pattern, dual-write, and traffic shadowing.”
SaiTeasmitha Kaja
Mid-level Full-Stack Software Engineer specializing in Java/Spring Boot and cloud microservices
“Backend-focused Python/Flask engineer who has built authentication/profile services with clean modular architecture (blueprints + service layer) and tuned SQLAlchemy/Postgres for scale using indexing, query rewrites, and pagination. Has production-style integration experience for AI/ML via TensorFlow Serving and OpenAI APIs (batching, rate limiting, caching), plus multi-tenant data isolation and high-throughput background processing with Celery/Redis and idempotent jobs.”
“Backend/AI engineer who built a real-time vector database system for high-frequency financial data using Kafka/Flink on Kubernetes, achieving sub-100ms similarity search at 10k+ concurrent load and resolving tricky duplication issues with idempotency/versioning. Also shipped an end-to-end LLM-based travel itinerary feature (profiling + prompt workflows + APIs) with a focus on quality consistency and low latency.”
Adithya Chittajallu
Mid-level AI/ML Engineer specializing in LLM systems, MLOps, and Healthcare AI
“Built and shipped a production-grade agentic RAG system at CVS Health for patient adherence and medication recommendations, processing 20k+ patient records/day. Strong focus on real-world reliability: hybrid retrieval tuned with re-ranking (<400ms latency), strict JSON/schema validation and tool guardrails, and monitoring/drift detection that reduced MTTD from 6 days to 18 hours while improving recommendation accuracy (+8%) and cutting escalations (~23%).”
Durga Mahesh Boppani
Mid Software Engineer specializing in distributed cloud-native backend systems
“Backend/AI workflow engineer who built production-grade orchestration systems for hardware security verification at Silicon Assurance (Nextflow/Python/Postgres) and a multi-agent LLM-driven regulatory code checking system at the University of Florida. Emphasizes reliability: strict plan/execute/verify boundaries, queue-based isolation, and strong observability/auditability with Prometheus/Grafana and persisted prompts/tool calls.”
“Full-stack product engineer with hands-on ownership across React/TypeScript, serverless backends, and Postgres, combining technical depth with strong UX instincts. Stands out for measurable impact: improved a slow query from seconds to under 200ms and increased onboarding completion by about 25%, while also building reusable platform primitives and scalable multi-tenant configuration systems.”
Pahuldeep Singh
Senior Full-Stack Developer specializing in scalable web platforms and automation
“Backend/full-stack engineer focused on TypeScript/Node.js systems, with hands-on ownership of a real-time telemetry and dashboard platform built on Kafka, Debezium, PostgreSQL, and GraphQL. Stands out for combining event-driven architecture, correctness/idempotency patterns, strong observability, multi-tenant security, and developer-friendly API design in production environments.”
Chanatip Vachirathanusorn
Entry-level Software Engineer specializing in full-stack and FinTech systems
“Software engineer with AppFolio payments experience who shipped an end-to-end payment adoption rollout during a short internship, including full-stack implementation, stakeholder coordination, and real-time monitoring. Also built an insurance service AI agent inspired by his father's agency, combining structured document ingestion, RAG, and agentic integrations to improve reliability on dense policy documents.”
Anirban Ghosh
Mid-level Machine Learning Engineer specializing in data science and cloud systems
“ML engineer who independently pitched and built a recommendation engine at Danske Bank in a legacy fintech environment, creating compliant data pipelines and deployment infrastructure from scratch and delivering a 62% engagement lift with 70%+ advisor adoption. Also worked at AWS on classification and GenAI-powered reporting systems, with strengths spanning production ML, platform setup, monitoring, and research-to-production optimization.”
Chaitanya Prasad Reddy Narala
Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems
“Senior AI/ML engineer focused on production LLM systems, combining RAG, fine-tuning, distributed training, and AI safety to ship scalable real-time moderation and conversational AI platforms. Stands out for pairing deep AWS/Kubernetes MLOps expertise with measurable impact: 40% lower latency/cost, 30-50% fewer hallucinations, and major reliability gains through observability and automation.”
Manish Challa
Mid-level AI/ML Engineer specializing in Generative AI and financial services
“ML/AI engineer with hands-on experience shipping regulated financial AI systems at JPMC and Capgemini, spanning credit risk, fraud detection, and generative AI assistants. Stands out for combining modern LLM/RAG architectures with strong MLOps, real-time infrastructure, and explainability/compliance practices, while delivering measurable business impact in latency, accuracy, cost, and risk reduction.”
Sachin Komati
Mid-level AI/ML Engineer specializing in GenAI, RAG, and healthcare ML
“Built an end-to-end GenAI/RAG platform for financial compliance and research at BlackRock, focused on safe, auditable answers in a highly regulated environment. Combines strong LLM engineering depth with production platform skills and delivered clear business impact, including reducing research/compliance turnaround from hours to seconds, improving retrieval relevance by 22%, and cutting inference costs by 75%.”
Farhan Shahbaz
Senior Software Engineer specializing in cloud infrastructure and platform engineering
“Backend engineer with deep experience in security and access-management platforms at JPMorgan Chase, including owning automation for migrating 50+ engineering teams from CyberArk to HashiCorp Vault. Stands out for combining regulated-environment rigor, infrastructure automation, and production operations with practical AI integration in internal access workflows.”
Mihir Sahu
Intern software engineer specializing in AI, full-stack, and applied ML
“Backend/ML-focused engineer with experience spanning fintech, sales enablement, and medtech, including a Capital One capstone and a Singapore medtech startup internship. Stands out for owning end-to-end AI/backend systems, from a GenAI sales pitch platform that cut prep time by 50% to an ultrasound-guidance MVP for non-expert operators in a highly ambiguous domain.”
Mohammad Gouse Ali Shaik
Mid-level Software Development Engineer specializing in cloud-native AI/ML systems
“AI/ML-focused engineer with practical experience building RAG-based and multi-agent systems, including architectures for retrieval, reasoning, context processing, and response generation. Stands out for combining LLM productivity gains with disciplined software engineering practices like validation, monitoring, and reproducibility.”
Anchal Thool
Mid-level Software Engineer specializing in cloud infrastructure and backend systems
“AI/ML-focused software engineer who has built and orchestrated multi-agent systems with separate retrieval, planning, validation, execution, and escalation components. Stands out for combining hands-on experimentation with a strong reliability mindset, using observability, structured logging, tracing, and evaluation to make agentic workflows production-ready.”
Deepthi Pamisetty
Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems
“Full-stack engineer with production experience building AI-powered search and automation systems at JPMorgan Chase and customer-facing product features at Wayfair. Stands out for combining React frontend work with backend microservices, RAG/LangChain AI integration, and cloud-scale performance tuning, including a support chatbot that reduced ticket resolution time by 35%.”
Pujan Parikh
Staff Front-End Engineer specializing in enterprise cloud and AI-driven applications
“Frontend-leaning architect with deep React/TypeScript expertise who has repeatedly owned platform-level problems across Oracle Health, Oracle Cloud, and Veritas. Stands out for building cross-plugin routing standards for a next-gen EHR, high-scale real-time observability dashboards, and even stepping into backend/AI infrastructure design with Kafka- and RAG-based systems.”
“Forward-deployed, full-stack/platform engineer who owns production features end-to-end across frontend, backend, data, and infrastructure (AWS serverless, Terraform, React). Has modernized critical fintech/payment systems (zero-downtime monolith-to-microservices with Kafka event sourcing) and productionized AI-native support workflows (LLM + RAG on Pinecone) with measurable gains in latency, incidents, CSAT, and support efficiency.”
Bhargav Patel
Mid-level Full-Stack Developer specializing in AI-driven FinTech systems
“Built and shipped AI-powered fraud investigation and risk dashboard features at Barclays, combining full-stack engineering with RAG-based LLM systems in a compliance-sensitive environment. Stands out for pairing explainable AI architecture with measurable business impact: improving fraud detection accuracy by 18% while reducing analyst investigation time by 14%.”
Gaurav Sherwani
Mid-level product and data engineer specializing in FinTech and AI
“Data engineer and technical product manager with a strong pre-sales-style background in highly regulated financial environments, translating complex data infrastructure work into business cases, compliance signoffs, and leadership buy-in. At UBS, they drove a cloud migration with $2M savings and built regulatory automation that beat benchmarks, while at Omnic.ai they worked directly with users on a GPT-4 feature that doubled MAU and lifted subscriptions 20%.”