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Vetted REST API Professionals
Pre-screened and vetted.
“Software engineer with healthcare domain experience (patient monitoring and provider systems) who improves reliability and performance in complex React/Flask applications. Led API standardization for shared internal React utilities using an RFC + deprecation strategy, and optimized a live WebSocket dashboard to handle 3000+ concurrent clinics while reducing client CPU usage. Strong in production debugging, data ingestion validation, and operational improvements like structured logging and alerting.”
Rakesh Thota
Mid-level Data Engineer specializing in multi-cloud real-time data pipelines
“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”
Sreya Vallabhaneni
Junior Software Engineer specializing in distributed systems and cloud microservices
“Built and shipped an AI-driven interview evaluation pipeline at SeekOut that automated recruiter screening via a multi-stage LLM agent workflow (.NET backend, RabbitMQ orchestration, Python workers). Emphasizes production-grade reliability (idempotency, retries, strict JSON/schema validation), strong observability with OpenTelemetry, and measurable efficiency gains including ~40% reduction in token usage/cost.”
Saiprasad Barkam
Mid-level Data Engineer specializing in cloud ETL and streaming data pipelines
“Data engineer in healthcare/clinical data platforms (HarmonCare) who built and operated an end-to-end lakehouse pipeline ingesting HL7/FHIR at ~2–3M records/day on AWS (Glue/Lambda/S3/Spark) and serving trusted datasets in Snowflake. Implemented strong validation/reconciliation gates and a data quality framework that reduced discrepancies ~40%, plus CI/CD (GitHub Actions/Terraform) and monitoring (Airflow/CloudWatch).”
PRAHARSHA JANDHYALA
Mid-level Data Scientist/Data Analyst specializing in ML, BI dashboards, and ETL pipelines
“Data/ML practitioner with experience at Humana and Hexaware, focused on turning messy, semi-structured datasets into production-ready pipelines. Built an age-prediction model from book ratings using heavy feature engineering and multiple regression models, and has hands-on entity resolution (deterministic + fuzzy matching) plus embeddings/vector DB approaches for linking and search relevance.”
Prashanth Kedri
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and predictive maintenance
“ML engineer with General Motors experience deploying production AI systems, including a BERT-based sentiment classifier for over a million customer support call transcripts (reported ~91% precision) and sub-200ms latency via FastAPI/Docker optimization. Also built predictive maintenance models and automated retraining/monitoring workflows using Airflow and MLflow, collaborating closely with non-technical customer support stakeholders.”
xavier mendez
Mid-Level Full-Stack Software Developer specializing in React, PHP, and AWS
“Software engineer working on a benefits/deductions product, owning a fast-turnaround feature spanning multiple client/internal UI flows. Built a centralized service layer and a PHP validation pipeline supporting a React/TypeScript frontend, coordinated two other developers to deliver in parallel, and emphasized quality via test cases, documentation, and QC collaboration.”
Sabita Kumari
Senior Full-Stack AI Engineer specializing in LLM/RAG agentic systems
“Built and deployed JobMatcher AI, an LLM-driven workflow automation product for job seekers that extracts requirements from job descriptions, matches to user skills, and generates tailored outreach. Demonstrated strong production engineering by cutting per-run cost ~70%, improving reliability with retries/backoff/fallbacks, and reducing hallucinations via schema validation and templating; also orchestrated the system with LangGraph plus Docker Compose across API, vector DB, and workers.”
Anay Dongre
Junior Machine Learning Engineer specializing in GenAI and LLM fine-tuning
“Robotics software engineer focused on hard real-time autonomy for legged robots, building a quadruped navigation stack that combines vision SLAM with MPC and maintains a deterministic 500Hz control loop. Deep performance optimization experience across CUDA (sub-2ms perception latency), ROS 2/DDS real-time tuning, and motion planning (cut 500ms spikes to sub-5ms). Also designed distributed ROS 2 + Zenoh communications between quadrupeds and aerial drones and validated robustness under lossy wireless conditions.”
Ambuk Rehani
Mid-level AI/Backend Engineer specializing in RAG and data platforms
“Built and shipped a production LLM-powered financial Q&A interface that extracts precise numeric data from PDFs using a hybrid AWS Textract + LLM normalization pipeline, with confidence gating and guardrails to prevent unreliable answers. Experienced with LangChain-based RAG orchestration (chunking, memory, structured outputs) and collaborated closely with PMs/analysts on IRS Form 990 extraction requirements.”
Shrinivas Bhusannavar
Mid-level AI Engineer specializing in agentic LLM systems and RAG platforms
“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”
Bhavishyasai Chigurupati
Mid-Level Data/ML Engineer specializing in Generative AI and cloud data platforms
“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”
Supriya Thorat
Mid-level Software Engineer specializing in full-stack web and cloud automation
“Full-stack TypeScript/Angular/Node engineer who owned a production healthcare application for a pharmaceutical client, supporting 100K+ monthly users across 10+ countries. Strong focus on maintainability and quality (reusable localized component library, ~90% unit test coverage, SonarQube in CI/CD) plus performance work (reported 15% client-side latency reduction and up to 50% backend latency reduction) while migrating legacy mobile code with strict backward compatibility.”
Jarin Tasnim
Staff/Lead Software Engineer specializing in distributed data and ML platforms
“Defense-domain AI engineer who built a production ReAct-style RAG system for military training data/material generation, scaling to ~1000 users and cutting generation time by 50%. Also has experience designing GPU-cluster parallel computation with PyTorch and handling production incidents involving database performance and schema design.”
Kavya Myakala
Mid-level Frontend Developer specializing in security analytics dashboards
“Built and shipped production LLM agents including an end-to-end customer support resolution system (99.9% uptime target) that improved customer satisfaction by ~18% and reduced the need to scale support headcount. Demonstrates strong agent engineering fundamentals—tool-based orchestration, schema-first structured outputs with deterministic validation, and robust eval/monitoring loops—plus experience integrating agents with messy ERP data using canonical normalization and safe fallbacks.”
“Backend/data engineer with healthcare claims expertise who has owned production data pipelines end-to-end, including ingestion, validation, transformation, and API serving. Stands out for improving data quality by 30%, building reliable integrations with strong auditability, and setting up pragmatic cloud deployment and observability in ambiguous early-stage environments.”
Ido Avnon
Junior Software Engineer specializing in AI, geospatial, and full-stack applications
“Early-stage startup engineer with experience at pre-seed and roughly Series A-stage companies, including Rage Events Inc. Shipped production full-stack features with Next.js/TypeScript, designed Postgres-backed workflow systems, and took ownership of post-launch support and fixes in fast-moving environments.”
Jeet Patel
Junior AI and Backend Engineer specializing in LLM systems
“AI/LLM engineer who has shipped production RAG copilots and multi-agent workflows, including a real-time Llama3 (Ollama) copilot backend handling 12k+ concurrent queries at 99.9% uptime. Deep on orchestration (Langflow/Airflow/Kubernetes), reliability evaluation (hallucination detection, p95 latency, token cost), and monitoring (Prometheus/Grafana), with demonstrated stakeholder-facing analytics delivery via Tableau.”
Lavanya Nandan Deole
Intern software engineer specializing in AI, mobile, and distributed systems
“Entry-level candidate who built NYC Lens, a real-time Gemini-based multi-agent system that processes live camera input, identifies landmarks, and returns structured contextual insights. Despite being a fresher, they show hands-on experience with deployment on Cloud Run, modular orchestration, noisy-data handling, and reliability patterns like retries, fallbacks, and explicit state management.”
“Gameplay engineer with hands-on ownership of social/multiplayer Unity systems, including a visual scripting tool that unlocked user-generated worlds and expanded content from 3 internal maps to dozens of player-created experiences. Has shipped across VR and mobile, worked with Photon PUN networking, and is also experimenting with LLM-powered product features via a Go/AWS backend.”
Arslan Javed
Senior Machine Learning Engineer specializing in LLMs, NLP, and computer vision
“Built and owned production GenAI systems for both infrastructure automation and customer support. Most notably, they created a self-healing multi-cloud incident response system that automated 65% of tier-1 alerts and reduced application crashes by 75%, and also shipped a hybrid RAG support triage agent that automated 60% of tier-1 inquiries with human escalation guardrails.”
Sreeraj Chintham
Mid-level Software Engineer specializing in Python backend and AI/GenAI
“Backend/infrastructure-focused engineer building AI-agent products for small businesses, including a customer-service agent platform with intent routing, RAG over Pinecone, and external booking API integration. Has shipped Python/FastAPI services with JWT auth, versioned APIs, Docker deployments to AWS EC2 via GitHub Actions, and production monitoring with Prometheus/Grafana.”
Tarun Abhaye Thiagarajan
Mid-level Solutions Architect specializing in Enterprise AI and SaaS
“Enterprise implementation/deployment specialist focused on HRMS and payroll systems across APAC customers, combining cloud/hybrid (AWS/Azure/GCP) integration work with strong client-facing delivery. Demonstrated ability to debug complex production issues across application, database, and network layers (e.g., isolating VPN/router congestion) and to tailor Python-based data cleaning/scoring/utilities to customer-specific workflows.”