Vetted Batch Processing Professionals

“Backend/platform engineer who owned an end-to-end financial data ingestion and validation system (Python/Django/FastAPI, Postgres, AWS), including large-file performance tuning, auditability, and CI/CD. Strong Kubernetes/EKS + ArgoCD GitOps practitioner and has delivered both Kafka-based real-time transaction streaming and a legacy on-prem stack migration to AWS (ECS Fargate, RDS, S3, Secrets Manager) with controlled cutovers and data consistency validation.”

“Backend-focused Python engineer who has owned production FastAPI services deployed on Kubernetes, including CI/CD (GitLab CI to ECR) and GitOps delivery via ArgoCD/Helm. Has hands-on experience with complex reliability and infrastructure work—solving data inconsistency with validation/partial-data paths, fixing K8s liveness issues via lazy loading, and supporting a phased cloud-to-on-prem migration with dual-writes and monitoring. Also built Kafka-based real-time ingestion consumers handling bursty, high-throughput traffic with async processing and topic/retention tuning.”

“Backend/data engineer focused on production-grade Python services and AWS platforms: builds FastAPI microservices on EKS with strong reliability patterns, CI/CD, and observability. Also delivers AWS Glue/Redshift analytics pipelines with schema-evolution and data-quality safeguards, and has modernized legacy batch processing into maintainable services with parallel-run parity validation and feature-flagged rollouts.”

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“Master’s-program backend engineer with strong Java/Spring Boot industry experience who also owned a Python analytics service (Flask/Postgres, JWT, Celery/Redis) and optimized large-dataset performance via SQL/batching. Has hands-on Kubernetes microservices deployment and GitLab+Terraform CI/CD/GitOps workflows, plus experience supporting phased on-prem to AWS migrations and building Kafka-based real-time streaming pipelines.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

“ML/NLP practitioner with healthcare payer experience at HCSC, focused on connecting messy unstructured clinical notes to structured claims/provider data to improve fraud-analytics workflows. Has hands-on experience fine-tuning transformers in AWS SageMaker, building large-scale embedding search with FAISS, and implementing robust entity resolution using golden datasets, precision/recall calibration, and production monitoring for drift.”

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”

Kush Shah

Screened

Junior Full-Stack Software Engineer specializing in web apps and automation

Remote, USA3y exp

“Backend engineer with hands-on experience building an AI-powered document processing pipeline for insurance workflows from design through deployment and production support. They combine LLM-based extraction with rule-based validation, retries, and observability, showing a pragmatic approach to making AI systems reliable in high-stakes environments.”

“Backend-focused software engineer building distributed, event-driven Java/Spring Boot microservices with Kafka for low-latency, high-frequency processing. Has hands-on experience modernizing a legacy Java system into containerized microservices deployed on Kubernetes with GitHub Actions CI/CD, and has integrated retrieval-based AI components into production workflows; no ROS/robot hardware experience yet.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“Backend engineer who has shipped both high-throughput real-time systems and production LLM/RAG features. Built a database-free, local-first messaging service (Node/Express/Socket.IO) achieving ~1,500 msgs/sec at <25ms p95, and implemented a Go-based RAG recommendation pipeline with strict JSON/schema validation, catalog grounding, fallbacks, and eval loops that cut hallucinations to ~1–2% while reducing LLM costs ~60%.”

“Backend/platform engineer with Optum experience owning a production FHIR Member Access API aligned to CMS interoperability requirements. Built and scaled Spring Boot/HAPI FHIR microservices on AWS (Docker/Kubernetes) with zero-downtime CI/CD, and operated them with strong observability (Dynatrace, logs/metrics, alerting) and incident response. Also implemented a Kafka-based FHIR bulk data pipeline with schema versioning, idempotent processing, and reliable backfills/replays.”

Visaj Kapadia

Screened

Mid-Level Full-Stack Developer specializing in AWS and scalable web platforms

Santa Monica, CA5y exp

“Software engineer with hands-on AWS experience optimizing an email campaign delivery system—re-architected a monolithic worker into multi-threaded/multi-worker ECS components to boost throughput ~600% (5 to 35 emails/sec). Comfortable debugging production issues (e.g., SQS/EventBridge policy misconfiguration) and emphasizes maintainable delivery via design docs, TDD, versioned APIs, and strong test coverage.”

Srichandan Kota

Screened

Senior Full-Stack AI Engineer specializing in Generative AI and FinTech

Minneapolis, MN6y exp

“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”

Abhinav Garg

Screened

Senior Robotics Software Engineer specializing in ROS 2 autonomy and distributed systems

College Park, MD6y exp

“Robotics Software Engineer with 2.5 years at the Army Research Lab building production tools and cloud infrastructure for large-scale ROS/Unity simulation on AWS. Created a Python GUI to streamline analysis of massive (100GB) ROS bag/MCAP datasets and has deep ROS2/Nav2 performance debugging experience (executor/QoS/TF tracing). Also built an in-house ROS perception pipeline for an assembly-line use case, reaching 92% accuracy.”

Sumanth Gottipati

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech

New York, NY4y exp

“At Delta Airlines, built and shipped a production LLM-powered semantic search/troubleshooting assistant over maintenance logs and operational documentation using OpenAI embeddings and a vector database. Implemented hybrid ranking, query enrichment, and structured filters to improve relevance ~35% while optimizing latency via caching and vector tuning. Also designed a scalable Kafka + AWS (Lambda/SQS) ingestion pipeline with strong reliability/observability and an eval loop using real engineer queries and human review.”

Nidhip Patel

Screened

Mid-level Software Engineer specializing in AI/ML and full-stack systems

United States3y exp

“Backend Java engineer with strong platform/DevOps experience: modernized an insurance claims legacy monolith into DDD-aligned microservices, deployed containerized services on Kubernetes with Jenkins CI/CD and static analysis gates, and implemented GitOps using ArgoCD. Also led major AWS migration planning with dependency mapping and network monitoring to uncover hidden dependencies, and built Kafka-based real-time event streaming with schema-registry-driven evolution.”

“Backend/full-stack engineer with an unusual mix of web application delivery, zero-knowledge cryptography, and applied ML. They built a BioTrack system that improved reporting by 40%, and also shipped a privacy-preserving but compliance-aware ERC-20 transaction layer using Noir and ZK proofs, processing 1000+ anonymous compliant transactions in just 2 weeks.”

“Robotics software/vision engineer with hands-on experience building motion-tracking systems that fuse camera-based 3D tracking with IMU orientation to reproduce tool motion for automated spray painting. Has implemented ROS nodes/packages for Orbbec camera streaming and SAM3-based segmentation, plus CAN bus coordination between robots and Dockerized deployment for a pick-and-place robotic cell.”

“Backend/data engineer with production experience building Python FastAPI services and AWS-native data pipelines. Has delivered containerized and serverless workloads (ECS/EKS/Lambda) with Terraform-based IaC, strong reliability patterns (JWT/RBAC, retries/circuit breakers, observability), and AWS Glue ETL into S3/Redshift. Demonstrated measurable SQL performance wins (40–50s to <4s) and owned real pipeline incidents through detection, mitigation, and prevention.”