Vetted Monitoring Professionals

“Full-stack engineer with e-commerce experience who shipped and owned an order history dashboard using Next.js App Router/TypeScript, combining server components for SEO/perf with client-side interactivity via React Query. Has backend reliability experience (Temporal order-processing workflows, Postgres modeling/indexing, and payment API idempotency keys), and emphasizes production stability, observability, and zero-incident launches.”

“Data engineer with experience at Sony and Walmart building high-volume, near-real-time analytics and ingestion systems. Has owned end-to-end pipelines from Kafka/Spark streaming through S3/Parquet and Redshift/Looker, emphasizing data quality (Great Expectations), observability (CloudWatch/Azure Monitor), and reliability (Airflow SLAs, retries, checkpointing), including measurable performance and latency improvements.”

Mahesh Kumar Duvvuri

Screened

Senior Full-Stack Software Engineer specializing in microservices and cloud-native systems

New York City, NY4y exp

“Backend/infra engineer with experience across Nestle, J.P. Morgan, and Capgemini, combining ML systems work (YOLOv8/PyTorch object detection with TFLite edge deployment) with production-grade cloud/Kubernetes operations. Has delivered measurable impact via AWS migrations (25% cost reduction, 99.9% availability), microservice modernization (35% faster processing), and low-latency Kafka streaming for financial dashboards (<100ms) using DLQs and idempotent consumers.”

Venkata Phaneendra

Screened

Mid-Level Java Full-Stack Developer specializing in cloud-native microservices

United States6y exp

“Full-stack engineer with production ownership across React/TypeScript, Node/Express, and Postgres, including zero-downtime releases and rollbackable migrations. Demonstrated measurable performance wins (20% response-time reduction) through DB query profiling and batching, plus hands-on AWS operations (ECS/Lambda/CloudWatch) and reliability patterns for ETL (retries, DLQs, idempotency). Experience shipping microservices quickly in ambiguous, fast-paced environments (Deloitte).”

Poojitha MaheshShyla

Screened

Mid-level Java Backend Developer specializing in cloud-native microservices

Sunnyvale, CA5y exp

“Backend-leaning full-stack engineer with Walmart experience building and operating high-volume media upload and processing systems. Strong in Java/Spring Boot, Postgres performance tuning (EXPLAIN/ANALYZE), and durable workflows using Kafka/Spring Batch with retries and idempotency, plus production ownership via monitoring and optimization; familiar with Next.js/TypeScript and modern React performance patterns.”

Ruthvik Bacha

Screened

Mid-level Data Engineer specializing in financial data pipelines and reliability

North Carolina, USA7y exp

“Systems/robotics-oriented software engineer focused on real-time orchestration and reliability: built a central control layer coordinating multiple concurrent agents with safe state machines, failure isolation, and recovery. Has hands-on ROS/ROS 2 integration experience in simulation (DDS/QoS, lifecycle, nodes in Python/C++) and emphasizes observability (structured JSON logs, correlation IDs) and low-latency control-loop performance under load.”

Sanjana Duvva

Screened

Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps

5y exp

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“Data platform engineer focused on large-scale, real-time fraud systems, with hands-on ownership of streaming architectures using Kafka, Spark, Snowflake, and Databricks. Stands out for combining performance tuning and platform automation with LLM/RAG-based enrichment, delivering measurable gains in latency, fraud accuracy, false positives, and analyst decision speed.”

Harrishkumar Loganathan

Screened

Mid AI/Machine Learning Engineer specializing in FinTech and Generative AI

Remote, USA3y exp

“AI/ML engineer with hands-on ownership of enterprise LLM deployments at Freshworks, including a large-scale RAG chatbot serving 15,000+ users across six departments. Stands out for combining deep production engineering skills—AWS microservices, Kubernetes, observability, retrieval quality, and faithfulness evaluation—with strong cross-functional stakeholder leadership and prior large-scale fraud data pipeline experience at Socure.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“ML/AI engineer with hands-on ownership of production computer vision, speech, and legal RAG systems. Notably improved a key-duplication CV pipeline enough to unblock commercial launch and remove specialist manual measurement, and also shipped a live Quran recitation detection feature for a product with 1M+ users.”

“State Street engineer who identifies operational pain points and turns them into high-impact internal platforms, including a service-health monitoring system and a Databricks log standardization pipeline used by 200+ users. Also experiments with practical LLM workflows, having built a Claude-based AI host that dramatically reduced facilitation time for a growing book club.”

“Founding engineer who has built web products end-to-end in startup settings, spanning FastAPI/React application development, auth, cloud deployment, and Kubernetes-based scaling. Particularly notable for designing custom GPU autoscaling for an AI-style recommendation product and later shipping workflow-driven healthcare support tooling using Temporal, Postgres, and modular backend logic.”

“Candidate brings practical GenAI engineering experience with a disciplined approach to AI-assisted development. They have designed lightweight multi-agent workflows for a RAG-based support copilot, including retrieval, relevance validation, response generation, and groundedness checks to reduce hallucinations.”

Shivoham Angal

Screened

Entry-level Backend Software Engineer specializing in AI and cloud systems

Remote, New York1y exp

“Backend-focused engineer who built a hackathon trading vault (AntiSwan) integrating the Polymarket CLOB client and applying the Kelly Criterion for allocation decisions. In an internship at StartupU, owned pre-launch monitoring by building Azure dashboards and Terraform/KQL-driven alerts with Microsoft Teams webhook routing, and previously automated a DynamoDB cross-region migration with integrity checks.”

“Full-stack engineer who has built both a TypeScript-based HR/payroll platform and a production agentic AI support system end to end. Stands out for combining strong product judgment with deep LLM systems thinking: RAG architecture, confidence-based routing, evals, observability, and human-in-the-loop design in a greenfield environment.”

“Backend-leaning full-stack engineer with PayPal experience building payment orchestration, settlement, and merchant risk systems at production scale. Stands out for combining cloud-native AWS delivery, database/query performance tuning, and reliability work in event-driven microservices, including a monolith-to-microservices migration that doubled deployment frequency and cut incident response time by 40%.”

Manoj Shinde

Screened

Senior Full-Stack Engineer specializing in cloud-native AI and FinTech systems

San Francisco, CA9y exp

“Full-stack engineer who has owned customer-facing reporting products end to end and also helped ship MemberGPT, an AI assistant for financial users. Brings a practical mix of React/TypeScript and Java/Spring Boot experience, plus hands-on LLM integration, retrieval grounding, evaluation, and production monitoring in a higher-trust financial context.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Capgemini engineer who helped take a travel insurance platform from prototype demos to a stable production system by clarifying requirements, hardening API contracts, and adding validation/logging to handle real customer data and external integrations. Experienced in real-time troubleshooting of complex workflows (including LLM/agentic-style workflows) through strong observability practices, and in leading practical developer-focused demos that accelerate client integration and adoption.”

“Built and deployed an enterprise GenAI knowledge assistant over thousands of internal PDFs/reports using a RAG stack (GPT-4 + Hugging Face embeddings + vector DB) to reduce manual search and SME escalations. Uses LangGraph/LangChain to orchestrate modular agent workflows with relevance filtering and fallback handling, and applies rigorous evaluation (golden datasets, edge cases, A/B tests) with production monitoring metrics.”

“Engineer with banking and industrial/IoT experience who has deployed a payment-processing microservice with zero downtime, handling Protobuf schema evolution and sensitive data migration via dual-write/checksum techniques. Demonstrates strong cross-stack troubleshooting (pinpointed intermittent distributed timeouts to a failing ToR switch port) and customer-facing Python ETL customization using plugin-based parsers and Pydantic validation, plus hands-on monitoring/alerting improvements with operators.”

“Full-stack engineer with primary depth in .NET Core and Python who has built and deployed end-to-end AWS applications (Lambda, API Gateway, S3, CloudFront) and supported them in production. Experienced in scaling large, data-driven workloads using queues/background workers, batching, and database tuning, with strong focus on API contracts, observability, and resilience patterns; also has hands-on React/TypeScript and some Spring Boot exposure.”