Vetted REST API Professionals

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Backend-focused engineer who owned an end-to-end Python/Flask service at Viasat powering a 1000+ user internal React app, including API design, Postgres performance tuning (~50% faster), Dockerization, and CI/CD. Demonstrated strong problem-solving by building custom EDN parsing logic and has built near real-time AWS SQS/Lambda pipelines with DLQs and autoscaling patterns; currently ramping on Kubernetes/GitOps (ArgoCD).”

“Backend engineer who owned an order management API built with Python/FastAPI and PostgreSQL, integrating payment and shipping providers with strong reliability patterns (idempotency, async workers, retries/backoff, circuit breakers). Experienced deploying services to Kubernetes using a GitOps model with ArgoCD (auto-sync, self-healing, pruning, rollbacks) and building high-volume Kafka streaming pipelines. Has also supported phased cloud-to-on-prem migrations with a focus on security monitoring/SIEM log continuity.”

“Built an AI-powered loan automation dashboard using React and open-source JavaScript libraries, with hands-on experience improving real-world performance by reducing re-renders and optimizing/caching multiple API calls. Also produced developer-friendly API documentation for a voice assistant project, helping teammates integrate features faster with fewer errors.”

“Built ReflectlyAI, an AI-powered interview coach, implementing a low-latency Python/Flask backend with modular LLM/Whisper services, retries/fallbacks, caching/batching, and async/background processing. Demonstrates strong PostgreSQL/SQLAlchemy performance tuning (EXPLAIN ANALYZE, composite indexes, selectinload) and multi-tenant isolation patterns (tenant-scoped schemas, tenant_id middleware), reporting ~50% response-time reduction.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Software engineer who built and open-sourced reusable React/Node.js modules (chat, auth, caching) from an AI education platform, emphasizing intuitive APIs and strong documentation. At TCS, improved a healthcare scheduling platform by diagnosing SQL/server bottlenecks and redesigning database + caching, cutting appointment load times by ~40% and reducing support complaints.”

“Software engineer with experience at Wipro Technologies and Wells Fargo building React-based SPAs, reusable component libraries, and developer documentation. Demonstrated strong performance engineering (React.memo, list virtualization, code splitting) with reported >50% rendering-time improvement, plus hands-on production support by diagnosing API outages via monitoring/logs and implementing traffic/server fixes. Comfortable leading workstreams in fast-changing environments using Kanban and tight stakeholder feedback loops.”

“ML/MLOps practitioner from Discover Financial who built and deployed a real-time AI fraud detection platform (LSTM + VAE) on AWS SageMaker with Docker/FastAPI and Jenkins-driven CI/CD. Demonstrated measurable impact (30% accuracy lift, 25% fewer false alerts) and deep expertise in class-imbalance mitigation, drift monitoring, and orchestration (Airflow/Kubeflow), plus strong stakeholder adoption via Power BI dashboards for fraud/compliance teams.”

“iOS full-stack/mobile engineer who built a SwiftUI (MVVM) barcode-scanner app using VisionKit for on-device, low-latency recognition, focusing on responsiveness via continuous scanning and debouncing. Also has Capgemini customer-facing support experience resolving restored-file access/permissions issues and receiving positive CSAT feedback.”

“Customer-facing technical professional with experience spanning engineering and product who advises on application security tradeoffs (threat modeling, API/auth risks, SOC2 mapping) and drives pragmatic remediation plans. Hands-on with Kubernetes/CI-CD agent integrations, secrets management, and log-driven troubleshooting; documented and escalated complex customer environment issues and reported a 40% reduction in bug reporting through workflow automation.”

“Full-stack engineer with healthcare and fintech experience who has owned production features end-to-end—most notably an AI assistant clinical risk summary tool on AWS (FastAPI/Lambda + React/TypeScript) that cut analyst review time ~40%. Strong in performance tuning for large datasets (S3/Athena), production ops/observability (CloudWatch, CI/CD, env separation), and building reliable ETL/integrations with idempotency and retries.”

“Backend/full-stack engineer with experience building internal government and banking platforms, including a branch operations management system at Bank of China (React/Apollo + Node GraphQL + MongoDB) and a capital project management service for the State of Alabama (Spring Boot + PostgreSQL). Emphasizes maintainable API design, strong validation/data integrity, RBAC/JWT security, and production reliability through clear service boundaries, logging, and monitoring.”

“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”

“Built and monetized an object-identification app end-to-end (FastAPI backend, HTML/JS frontend, SQLite→Postgres, auth, and an iOS wrapper via Capacitor/Xcode with Apple privacy/policy compliance). Also productionized an AI-native geospatial metadata/QA assistant using LLM+RAG plus deterministic Python validation, measuring impact via time-to-first-pass review and rework rate, and has experience modernizing legacy GIS workflows and delivering across USDA/FEMA-style teams with disciplined Jira-based execution.”

Indupriya gummadi

Screened

Mid-level Full-Stack Developer specializing in React/Next.js web applications

Detroit, MI5y exp

“Software engineer with airline operations domain experience building real-time dashboards and internal tools for frontline ops and customer support. Strong in TypeScript/React performance optimization and backend/microservices reliability (RabbitMQ, DLQs, autoscaling, correlation IDs/observability), with a track record of shipping incrementally via feature flags and driving organic adoption through pilot-led iteration.”

Meghana Nandivada

Screened

Junior Machine Learning Engineer specializing in production ML systems and MLOps

2y exp

“ML/AI engineer (TCS) who built and productionized a customer segmentation and personalized-offer recommendation pipeline end-to-end (data cleaning/feature engineering/clustering through Flask API deployment in Docker with monitoring). Emphasizes reliability and operational rigor via validation checks, periodic retraining, model/API versioning, and latency optimization, and has experience translating marketing KPIs into usable dashboards for non-technical teams.”

Akhil Bharadwaj Mateti

Screened

Mid-level Software Engineer specializing in Data Science and Machine Learning

Arlington, Virginia4y exp

“Robotics/AV perception engineer who built a semantic-segmentation road detection system and integrated it into a ROS-based real-time pipeline (ROS bag camera feed to live monitor) achieving ~12 FPS. Strong in practical deployment work: solved multi-library versioning issues (ROS/OpenCV/TensorFlow), containerized the stack with Docker, and optimized inference by shifting runtime to C++ for large latency gains on NVIDIA hardware.”

Pooja Miryala

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for banking and healthcare

Ohio, USA4y exp

“Deployed a real-time LLM-driven call center summarization and agent-assist platform at Fifth Third Bank, combining transformer models (BERT/GPT) with FastAPI inference on AKS and vector storage (ChromaDB/PostgreSQL). Emphasizes production-grade reliability (autoscaling, CI/CD, monitoring) and measurable evaluation (A/B testing), and translates model outputs into business-facing Power BI insights for call center leadership.”

ammar mohammed

Screened

Junior Full-Stack Web Developer specializing in modern JavaScript frameworks

Cairo, Egypt1y exp

“Frontend-focused engineer (also capable on backend) who led an end-to-end e-commerce project and has built 3–4 admin dashboards across e-commerce, real estate, and invoicing/branch-management contexts. Demonstrates practical decision-making around React/TypeScript state management (Redux/Zustand/Context) and performance optimization in React/Next.js (memoization, lazy loading, image optimization), with an emphasis on usable UX and rapid delivery via mock-data-driven parallel FE/BE development.”

Sai somapalli

Screened

Senior LLM Engineer specializing in Generative AI, RAG, and multimodal assistants

USA6y exp

“GenAI/NLP engineer with experience building classification and summarization pipelines in PyTorch and deploying multimodal GPT-4-style workflows. Has integrated LLM applications across OpenAI, Azure OpenAI, and Amazon Bedrock, and uses LangChain/LlamaIndex/Semantic Kernel to orchestrate RAG and agent workflows with production-focused evaluation metrics like task success rate and groundedness.”

Piyush Rajendra

Screened

Mid-level AI/ML Engineer specializing in production RAG systems and MLOps

Athens, GA4y exp

“Built and deployed a GPT-4 + Pinecone RAG system that lets users query large internal document collections with grounded, cited answers. Demonstrates strong applied LLM engineering (chunking experiments, hallucination controls, metadata recency boosting) plus production-minded evaluation/monitoring and performance tuning (rate-limit mitigation via pooling/batching). Also effective at translating complex AI concepts to non-technical stakeholders through prototypes and live demos, helping secure client sponsorship.”

Daanesh Potnuri

Screened

Mid-Level Full-Stack Engineer specializing in API-driven microservices and cloud delivery

5y exp

“Software engineer with hands-on experience building a decentralized file-sharing dApp, bridging a React frontend with Ethereum smart contracts via Web3.js and integrating IPFS for decentralized storage. Demonstrates a rigorous, measurement-driven approach to performance optimization (profiling + benchmark/regression loop) and strong ownership in high-stakes environments, including Fircosoft sanctions platform optimization and rapid production hotfixes for user-impacting issues.”