Vetted Distributed Tracing Professionals

“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”

Naveen Chokkakula

Screened References

Senior Golang Engineer specializing in distributed systems and cloud-native microservices

Texas, USA5y exp

“Software engineer with experience at UWorld and Infosys, focused on full-stack product delivery and backend reliability. Stands out for owning analytics/onboarding experiences end-to-end while also building resilient distributed systems, reusable service primitives, and safe multi-tenant configuration guardrails.”

YESHA BHAVSAR

Screened ReferencesStrong rec.

Intern Software Engineer specializing in full-stack and AI applications

Alpharetta, GA0y exp

“Built and deployed an "AI Closet" application as a personal product, owning full-stack and applied AI features end to end. Particularly interesting for recruiters because they combined Next.js/TypeScript product engineering with practical AI systems work, including OpenAI vision-based smart form fill, personalized recommendation learning, and a grounded RAG application with evaluation and regression testing.”

Nidhi Sharma

Screened ReferencesStrong rec.

Junior Software Engineer specializing in backend systems, AI, and cloud infrastructure

Noida, India2y exp

“Built multiple AI-heavy systems with a strong engineering lens on observability, reliability, and real-world usability, including an LLM gateway for auditability/failure isolation and Allyvision, an accessibility tool for visually impaired users. Also owned an end-to-end warehouse shipment tracking dashboard at Addverb Technologies that drove measurable operational gains, combining backend/data depth with frontend product execution.”

“Backend/data engineer with production experience across event-driven Python ingestion services on AWS (EventBridge/SQS/MongoDB), serverless APIs (Lambda/API Gateway), and analytics ETL (Glue → Redshift). Has modernized legacy reporting into Node.js/React systems and demonstrated measurable SQL performance wins (minutes to seconds) plus strong incident ownership with validation, DLQs, and alerting.”

“Backend-leaning full-stack engineer who builds and operates Spring Boot microservices with React/TypeScript frontends, using Kafka/RabbitMQ for event-driven workflows. Created an internal ops dashboard for Support/SRE with tracing, alert correlation, and self-serve actions, improving MTTR and reducing escalations while maintaining regulatory-grade reliability and security.”

“Full-stack engineer who built an early-stage social platform for scriptwriters (Scriptscape) from scratch, owning everything from React Native/React UX to Node/Postgres APIs and AWS deployment. Demonstrates strong production-minded engineering with CI/CD, observability, and scalability patterns (cursor pagination, indexing, background jobs), plus experience hardening flaky third-party integrations with idempotency and backfills.”

“Built a production "Mini RAG Assistant" for internal document Q&A, focusing on grounded answers (anti-hallucination), retrieval quality, and latency/cost optimization. Uses LangChain/LangGraph for orchestration and applies a metrics-driven evaluation loop (including reranking and semantic chunking improvements) while collaborating closely with product stakeholders.”

“Master’s program project lead who built and deployed a real-time sound recognition system (Flask + React Native + ML) that was adopted by 200+ university students. Demonstrates strong production engineering and cross-layer debugging—solving latency, unreliable uploads, and observability gaps using microservice separation, chunked/idempotent transfers, and packet-capture-driven network diagnosis—plus AWS/on-prem and IoT edge-to-cloud integration experience.”

“LLM/RAG-focused engineer who repeatedly takes agentic workflows from impressive demos to dependable production using rigorous evals, SLOs, and deep observability. Has led high-impact incident mitigation (22-minute MTTR during a major sale) and developer enablement workshops, and partnered with sales to close a $410k ARR enterprise deal with a tailored RAG pilot (FastAPI/pgvector/Okta/InfoSec-ready).”

Raghavendra Dubey

Screened

Mid-level Software Engineer specializing in Java microservices and cloud-native systems

CA, USA5y exp

“Enterprise workflow/product engineer (DXC) who owned a customer-facing workflow application for 500+ users and improved performance ~30% through API/SQL optimization, caching, and CI/CD-backed iteration. Experienced designing React/TypeScript + Java/Spring Boot systems and operating microservices with RabbitMQ/Kafka-style messaging, emphasizing reliability via DLQs, backpressure, and strong observability. Also built an internal automation dashboard adopted by support/ops teams to cut manual work and reduce SLA misses.”

“Full-stack product engineer who builds and owns production features across Next.js/React/TypeScript and Java Spring Boot, with strong Postgres data modeling and performance tuning. Has delivered measurable improvements (60%+ faster renders, 2s→100ms queries, 50% lower workflow latency) and built reliable Kafka-based workflows with robust observability (Prometheus/Grafana/Alertmanager) and high test coverage.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

Mayur Agrawal

Screened

Entry-Level Backend Engineer specializing in analytics automation and cloud data pipelines

Bengaluru, India1y exp

“Forward Deployment Engineer focused on application security and production integrations, with hands-on experience hardening API-driven ticketing systems (JWT/RBAC/rate limiting/log redaction) and implementing CI/CD security controls (Bandit SAST, SCA, container hardening). Strong in diagnosing peak-load production issues using logs/metrics/infra signals and driving durable fixes like adaptive throttling and backoff, while aligning engineering, business, and leadership stakeholders on risk and SLA impact.”

Vishesh Kumar

Screened

Intern Software & AI Engineer specializing in distributed systems and LLM applications

Palo Alto, CA1y exp

“Stony Brook Fall 2024 capstone contributor who built a ROS2-based warehouse mobile robot prototype, owning perception and SLAM integration end-to-end. Strong in real-time robotics optimization on Jetson Orin (TensorRT/CUDA, ROS2 tracing/Nsight) and in distributed ROS2 communications (DDS discovery/QoS, MAVLink-to-ROS2 bridging), with a full simulation/testing/deployment toolchain (Gazebo, CI tests, Docker/K3s).”

Yash Prakashbhai Shah

Screened

Senior Backend Software Engineer specializing in Java, microservices, and cloud infrastructure

Santa Clara, CA6y exp

“Backend/platform engineer at Aryaka Networks who built a centralized resiliency and security Spring Boot library to standardize Keycloak RBAC and fault-tolerance across 25+ Kubernetes-migrated microservices. Uses profiling and observability (Prometheus/Grafana) to drive measurable performance and reliability gains (25% faster APIs, 70% faster environment setup) and accelerates adoption via golden-path starter repos and Swagger/OpenAPI live docs.”

Purva Chakravarti

Screened

Senior Full-Stack Software Engineer specializing in scalable web apps, cloud, and blockchain/AI

Chino, California11y exp

“Full-stack engineer with strong production ownership across React/TypeScript, Node.js, and AWS (EC2/ECS/RDS/CloudWatch), including CI/CD, observability, and incident response. Delivered a secure RBAC workflow module end-to-end and achieved measurable gains (~30–40% latency reduction, ~50% error reduction) that lowered infra/ops costs. Comfortable in high-ambiguity startup environments—shipped a payment module within 2 days of joining with no documentation.”

“Full-stack product engineer who has built AI-assisted CRM and agent workflows in Project SARA and operational systems like payroll for a staffing platform. Stands out for combining React/TypeScript, Django/Postgres, real-time systems, and LLM orchestration with strong product instincts—delivering measurable gains in response time, conversion, and engineering leverage.”