Vetted REST APIs Professionals

Sri Harshitha Yannam

Screened

Junior Software Engineer specializing in AI/ML and cloud platforms

Austin, TX2y exp

“LLM/agent engineer who shipped a production "Memory Assistant" at HydroX AI, building a LangChain/LlamaIndex RAG memory pipeline on ChromaDB/FAISS with robust fallbacks (BERT/BART), prompt-injection mitigation, and 99.9% uptime monitoring. Also built a multi-step customer support agent using Rasa + OpenAI Assistants API with structured tool calling, guardrails, and human-in-the-loop escalation, and has experience hardening agents against messy ERP data via Pydantic validation, idempotency, and transactional outbox patterns.”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

“Built and stabilized production data pipelines in clinical genomics, including integrating a qPCR step into Baylor Genetics' workflow with a focus on reliability, turnaround time, and reducing manual intervention. Also has hands-on LLM production experience, creating a Python/OpenAI-based translation evaluation pipeline that reduced manual review time by 70% and improved scoring consistency.”

“CTO-oriented engineering leader who emphasizes building and mentoring teams while aligning technology work to real business needs through close collaboration with product stakeholders. Also has entrepreneurial exposure, including an alcohol-related startup and prior experience securing funding.”

“Built a production AI-powered insights platform for marketing teams analyzing large-scale social and news data, combining Java microservices, Kafka, Spark, React, and LLM-based retrieval workflows. Stands out for shipping customer-facing AI features with measurable gains in accuracy and latency, plus solid reliability practices for high-volume backend systems.”

“5-year .NET full stack developer who has applied AI-assisted development in an enterprise Cisco environment, using tools like GitHub Copilot and ChatGPT to accelerate microservice API delivery while maintaining architecture, security, and code quality standards. Notably reports a roughly 30% reduction in development time on a customer policy management/claims processing project through disciplined use of AI for boilerplate, testing, and design validation.”

“Candidate brings practical GenAI engineering experience with a disciplined approach to AI-assisted development. They have designed lightweight multi-agent workflows for a RAG-based support copilot, including retrieval, relevance validation, response generation, and groundedness checks to reduce hallucinations.”

“LLM/agent engineer who shipped a two-stage AI recruitment screening platform at Foursquare that automated resume ingestion through behavioral assessment, delivering an 85% reduction in screening time across 5,000+ applications with auditability and confidence-gated decisions. Also built a multi-agent benchmarking framework using MCP tool interfaces and a RAGAS + LangSmith evaluation/observability stack, including async re-architecture that cut production latency by 50%.”

“Built and shipped an end-to-end autonomous telemetry and log-triage product that combined LLM-based anomaly analysis, strict typed validation, and a React observability UI. Particularly compelling is their focus on making non-deterministic AI reliable in production at scale—500,000 daily requests and 99.9% uptime—while also translating complex AI output into a usable experience for non-technical teams during live outages.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Capgemini engineer who helped take a travel insurance platform from prototype demos to a stable production system by clarifying requirements, hardening API contracts, and adding validation/logging to handle real customer data and external integrations. Experienced in real-time troubleshooting of complex workflows (including LLM/agentic-style workflows) through strong observability practices, and in leading practical developer-focused demos that accelerate client integration and adoption.”

“Interned at Zonda where he built an AI-driven semantic search solution over ~280M housing/builder records. Iterated from local LLMs via llama.cpp quantization to a vector-embedding retrieval system, then boosted semantic accuracy with a custom spaCy NER layer and re-ranking, optimizing for latency through precomputation. Collaborated with economics-focused stakeholders to reduce manual document/paperwork time by enabling natural-language search over internal data.”

“PhD-trained engineer with ~3.5 years of consulting experience building simulation/ML-driven manufacturing software. Deployed an ML surrogate model as a .NET C# DLL integrated with MES workflows, and resolved a critical pre-production latency issue by redesigning serialization/storage. Also built Python-based integrations across CAD/CAE tools and cloud material databases using an XML data model, with a strong interest in digital twins and real-to-sim/sim-to-real robotics workflows.”

“Engineering leader/CTO-type with deep experience building and scaling a vehicle routing platform at Transdev On Demand, including a nationwide rollout to 22 US airports ahead of schedule. Drove engineering best practices (CI/CD, high test coverage, pair programming, automated deployments) and led a multi-tenant architectural upgrade to expand the routing engine to additional business lines and external customers.”

“GenAI Engineer at Infosys who built and deployed a production multi-agent RAG system for a top-tier bank, scaling to ~50,000 queries/day with 99.9% uptime. Drove measurable gains (45% accuracy improvement, 30% API cost reduction) through open-source LLM fine-tuning, Pinecone indexing/retrieval optimization, and AWS-based MLOps/monitoring, and has experience enabling adoption via developer workshops and customer-facing collaboration.”

“Backend engineer who built and owned an AI-powered SMS survey platform for a nonprofit serving at-risk communities (internet-limited users), using Cloudflare Workers + Twilio and a state-machine survey engine. Scaled it to ~10k active users with near-zero downtime, added English/Spanish support, and iteratively improved LLM behavior (Claude 3.7 Sonnet) to handle nuanced, real-world SMS responses reliably.”

“Financial-services AI engineer who shipped a production investment research assistant using RAG over internal research reports, SEC filings, and meeting transcripts, with a strong emphasis on truthfulness and guardrails. Built a structured evaluation loop (200+ golden test cases, RAG Triad metrics) that directly improved retrieval quality (e.g., fixing year-mismatch retrieval, boosting sensitive-query performance by 18% and cutting hallucinations to near zero) and scaled ingestion to ~10k messy documents with RabbitMQ + OpenTelemetry.”

Srinivas Matta

Screened

Mid-Level Full-Stack Software Developer specializing in cloud-native web platforms

Paducah, KY4y exp

“Software engineer at Capital One who owned and shipped AI-driven personalization and internal insights dashboards end-to-end, emphasizing fast iteration with feature flags and tight user feedback loops. Built a TypeScript/React + Spring Boot/Python document automation platform with compute-heavy NLP microservices, async workflows, and production-scale reliability/performance practices (Kafka/RabbitMQ-style queues, Redis caching, tracing).”

SAI RAKAM

Screened

Mid-Level Software Engineer specializing in backend microservices and FinTech payments

Remote, USA3y exp

“Capital One engineer focused on fraud and payments platforms, owning end-to-end services and internal tools used by fraud analysts. Built high-traffic Kafka/REST systems and real-time React/TypeScript dashboards (WebSockets, Redis), with strong emphasis on observability, idempotency, and scalable microservices. Successfully drove adoption of AI-assisted fraud classification by pairing transparency and manual overrides with measurable workflow improvements.”

Sayali Patil

Screened

Mid-level Python Full-Stack Developer specializing in Healthcare and FinTech

Everett, MA6y exp

“Backend engineer with hands-on experience building a fraud-transaction monitoring system in Python/Flask, architected as Dockerized microservices and integrated with Kafka for high-volume streaming. Demonstrates strong performance and reliability chops across PostgreSQL/SQLAlchemy tuning (EXPLAIN ANALYZE, N+1 fixes, bulk ops), multi-tenant data isolation, and scaling via background workers + Redis caching, plus real-time ML inference deployment using TensorFlow on AWS.”

Vaibhav Sharma

Screened

Mid-level Software Engineer specializing in AI/ML and data platforms

Remote, USA5y exp

“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”

Kanaka Chalam Volety

Screened

Staff DevOps/SRE Engineer specializing in AWS, Kubernetes, and GitOps

San Jose, CA24y exp

“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”

Yun-Ting Chiou

Screened

Junior Full-Stack Software Engineer specializing in TypeScript, React, and Java microservices

Chicago, IL2y exp

“Software engineer with finance-domain experience who built an internal transaction management system end-to-end at Prospect Equities (TypeScript/React Native + Java Spring Boot microservices on AWS), delivering 40% lower query latency and 73% operational efficiency gains. Has also designed Terraform-provisioned, SQS-based distributed systems and scaled workloads to 10,000+ concurrent users, including monolith-to-SOA modernization that cut internal review time by 47%.”

Akshit Modi

Screened

Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps

Remote, USA5y exp

“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”