Vetted Apache Cassandra Professionals

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

Rohit Vibhu Channananjundarya

Screened

Mid-level Software Engineer specializing in distributed systems and full-stack platforms

Chicago, IL6y exp

“Engineer who treats AI as a force multiplier rather than a replacement for judgment, with hands-on experience using tools like Claude Code, Cursor, Copilot, and Codex across planning, coding, testing, and review. Particularly notable for building a multi-agent PR review system that automated summarization, risk scanning, schema validation, and test suggestions, helping the team shift reviewer time toward architecture and business logic.”

“Java/Spring Boot engineer with startup-style ownership experience across e-commerce, banking, and healthcare analytics. Stands out for driving a monolith-to-microservices migration with Kafka that improved checkout reliability under peak load, while also contributing full-stack with Angular and supporting production operations end to end.”

“Full-stack engineer with experience at Vanguard, PNC, and Humana building customer-facing investment/banking flows and internal ops tools using Angular/React/TypeScript with Spring Boot microservices. Strong in shipping time-sensitive changes safely via automated testing/CI (JUnit/Mockito, Jenkins, SonarQube) and in operating event-driven microservices with Kafka (idempotency, retries, correlation IDs). Improved internal tool adoption by responding to ops/support feedback with query optimization and clearer search results.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Software engineer with experience at Walmart and Amex building customer-facing backend services and microservices at scale (RabbitMQ). Built an internal developer tooling platform integrating Figma with GitHub Copilot to automate consistent React component creation, adopted across multiple teams; emphasizes fast, safe iteration using metrics, feature flags, gradual rollouts, and automated testing.”

“Backend engineer who built Flask-based microservices for a high-throughput risk engine, using Kafka for streaming decoupling and Redis for low-latency caching, with PostgreSQL + Cassandra for mixed relational and time-series needs. Has hands-on experience productionizing ML inference (Azure OpenAI/TensorFlow) behind REST APIs with async queues, batching, and caching, plus multi-tenant isolation via schema separation and RBAC with per-tenant rate limiting.”

“Software engineer with Citi Bank experience building real-time fraud validation/scoring for loan processing, spanning Spring Boot microservices and a FastAPI Python service secured with OAuth2/JWT. Delivered React/TypeScript operations dashboards and deployed containerized services via Docker/Kubernetes with Jenkins CI/CD, while also tuning databases (Oracle/Postgres) and handling high-volume latency/scaling issues using ELK, caching, and autoscaling on AWS.”

“Providing technical guidance to a small team exploring a biomedical startup focused on earlier disease detection, including for remote/underserved areas. The venture is in ideation with initial research completed and is moving toward prototyping while exploring initial investment/support.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”

Sai Nekkanti

Screened

Mid-level Data Scientist / ML Engineer specializing in secure GenAI and financial compliance

Mount Laurel, NJ4y exp

“Built a production "sentinel insight engine" to tame information overload from millions of product reviews and support transcripts, combining Azure OpenAI (GPT-3.5) zero-shot classification with a fine-tuned T5 summarizer to generate weekly actionable product insights. Demonstrated strong MLOps/production engineering by adding drift monitoring with embedding-based detection, integrating REST with legacy SOAP/queue-based CRM via FastAPI middleware, and scaling reliably on Kubernetes with HPA.”

Sreelekha Vuppala

Screened

Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms

USA4y exp

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Data engineer who has owned end-to-end production pipelines processing a few million records/day, using Python/Airflow/SQL/PySpark with Snowflake serving to BI (Power BI). Built resilient external web data collection systems (anti-bot, schema-change detection, backfills) and shipped versioned REST APIs for internal consumers, improving pipeline success rates to 99% through monitoring, retries, and idempotent design.”

Ajay Desai

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and backend platforms

USA4y exp

“Built an AI-native legal research platform that automated analysis across 100,000+ dense legal documents, combining LLM workflows, async backend architecture, and conversational retrieval in production. Also brings cross-domain experience in investment-analysis agents and healthcare claims/billing systems, with a strong emphasis on reliability, deterministic orchestration, and safe handling of messy operational data.”

“Full-stack developer who uses AI thoughtfully as a productivity multiplier rather than a substitute for engineering judgment. Built a stock search platform with React, Node.js, and MongoDB, and has experimented with multi-agent workflows across frontend, backend, debugging, and documentation while keeping rigorous human review over logic, testing, and maintainability.”

“Full stack Python developer who actively integrates AI coding assistants into day-to-day engineering work, including code generation, debugging, testing, and documentation. Has also coordinated multi-agent workflows across backend, frontend, testing, and code review, showing an applied, productivity-focused approach to AI-enabled software delivery.”

Sanket Mungikar

Screened

Mid-level Software Engineer specializing in distributed backend and AI analytics platforms

California, USA4y exp

“Full-stack engineer at BigCommerce who combines customer-facing deployment ownership with hands-on AI/LLM systems work. Built and launched merchant analytics and predictive inventory workflows using React, TypeScript, FastAPI, Kafka, AWS, and RAG-style architectures, and has real production experience debugging non-deterministic AI issues caused by data pipeline freshness and event-ordering problems.”

Sai Divya Mulukala

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and distributed systems

USA5y exp

“Full-stack engineer with experience building operational dashboards at Walmart and improving digital banking experiences at Bank of America. Stands out for tracing performance issues across frontend, APIs, and backend services, including cutting response times from 1.2s to 700ms and resolving duplicate event-processing problems in distributed systems.”

“Full-stack and AI engineer with Capital One experience spanning real-time customer dashboards and production fraud-analysis systems. They combine TypeScript/Next.js/Node.js product engineering with LangChain-based RAG architecture over a 400 GB credit-report corpus, delivering measurable impact including 35% lower frontend latency and 45% faster analyst workflows.”

“Built and deployed a production LLM-powered clinical note summarization system used by care managers to speed review of 5–20 page unstructured medical records. Implemented safety-focused validation (prompt constraints, rule-based and section-level checks, human-in-the-loop) to reduce hallucinations while maintaining low latency and meeting privacy/regulatory constraints, integrating via APIs into existing clinical tools.”

“Full-stack developer who built an end-to-end Hotel Management System using React and Spring Boot with MongoDB and AWS. Has hands-on experience debugging API/data-fetching issues with Postman and validating results against the database, plus exposure to handling large data workloads with chunking and monitoring via Grafana/Tabula.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“LLM/agent engineer with production experience in the insurance claims domain, integrating OpenAI + LangChain into a claims platform to automate unstructured document extraction/classification and cut manual effort by 35%. Built reliable, fault-tolerant AWS/Kubernetes microservices with CloudWatch monitoring plus circuit breakers/retries/fallbacks, and implemented multi-step Spring Boot orchestration with schema validation, confidence gating, and human-in-the-loop handling for low-confidence cases.”

Molli Dinesh

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps

Remote, USA4y exp

“Built an AI-driven insurance policy summarization platform at Marsh, taking it end-to-end from messy PDF ingestion/OCR and custom extraction through LLM fine-tuning and AWS SageMaker deployment. Delivered measurable impact (25% reduction in manual review time, 99% uptime) and demonstrated strong production MLOps/LLMOps practices with Airflow/Step Functions orchestration, rigorous evaluation (ROUGE + human review), and continuous monitoring for drift, latency, and hallucinations.”