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Paras Yadav

@parasyadav1

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Backend engineer building reliable, low-latency distributed systems and ML infrastructure in Go and Python.

India

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What I'm looking for

I’m looking to build and scale reliable, low-latency backend systems and ML infrastructure, where operational visibility, correctness, and production resilience are non-negotiable—using Go/Python and strong distributed-systems practices.

I’m a backend engineer with 3 years of production experience building distributed systems and ML infrastructure. I focus on reliability, low latency, and operational visibility at scale, with strong hands-on work in Go and Python.

At CARPL.ai, I designed and scaled an event-driven backend with RabbitMQ and Redis, processing 1M+ tasks/month per on-prem deployment. I built an ML inference drift monitoring pipeline using statistical divergence metrics, migrated file upload services to gRPC to cut upload latency by ~30%, and optimized high-traffic APIs with Redis caching and async workflows—reducing average response latency from ~500ms to ~100ms.

In my more recent roles, I led migrations from AWS SageMaker to on-prem JupyterLab with custom IPython kernels to eliminate cloud compute and strengthen data-security compliance, and I improved scheduling reliability by replacing custom schedulers with Celery Beat. I also worked on Go-based worker services using goroutines and channels after migrating task dispatch from Django/Celery, and I’ve built observability foundations using the ELK stack and extended Orthanc (C++ ) to support new DICOM storage layouts.

Experience

Work history, roles, and key accomplishments

Current

Software Engineer - Contract

Current

Mercor

Feb 2026 - Present (4 months)

Performed deep technical reviews of Go and Python backend implementations, analyzing concurrency, API contracts, and edge-case handling. Authored behavioral specifications from test suites and code diffs to improve correctness, idempotency, and failure-handling in automated code generation.

Go Python Concurrency Test Suites Reliability Engineering

Senior Software Engineer

Carpl.ai

Apr 2025 - Jul 2025 (3 months)

Led migration from AWS SageMaker to an on-prem JupyterLab platform with custom IPython kernels, eliminating cloud compute/EFS/data-egress costs for large medical imaging workloads while strengthening data-security compliance. Replaced Python schedulers with Celery Beat (removing daily execution failures) and migrated the task dispatch layer from Django/Celery to Go using goroutines/channels to avo

Go Python AWS Sagemaker JupyterLab IPython Kernels Django Goroutines And Channels Data Compliance

Software Engineer

Carpl.ai

Apr 2023 - Mar 2025 (1 year 11 months)

Designed and scaled an event-driven backend using RabbitMQ and Redis, processing 1M+ tasks/month per on-prem deployment across medical imaging workflows. Built idempotent task execution with retries and prioritization (reducing partial-failure incidents ~85%), developed an ML inference drift monitoring pipeline using statistical divergence metrics, and migrated file uploads to gRPC to cut upload l

RabbitMQ Redis Celery Statistical Divergence gRPC Performance Tuning Async Flows

Software Engineer Intern

Carpl.ai

Jul 2022 - Aug 2022 (1 month)

Built centralized logging and monitoring for 6+ services using the ELK stack, reducing mean debug time from ~1 hour to under 10 minutes per incident. Modified and rebuilt Orthanc (C++) to support a restructured DICOM file storage layout, enabling new medical imaging workflows without upstream schema changes.

ELK Stack Elasticsearch Logstash Kibana Monitoring Debugging C++DICOM