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Vetted Java Professionals
Pre-screened and vetted.
Manish Abburu
Mid-level Software Engineer specializing in data pipelines, web scraping, and APIs
“Backend/data engineer who has owned end-to-end production pipelines and data services, processing ~500K–1M records/day from APIs/logs into MySQL and serving via REST APIs. Strong focus on reliability and data quality (ELK + structured logging/monitoring), with measurable improvements (~30% reduction in bad data, ~20% query performance gains) and experience operating external data collection/scraping systems with anti-bot and schema-change resilience.”
Pranav Mishra
Junior Machine Learning Engineer specializing in LLM agents, RAG, and MLOps
“AI/ML engineer who has shipped production systems across computer vision and conversational agents: built a YOLOv8-based wheel fitment pipeline at a Techstars-backed automotive startup, focusing on sub-second latency, monitoring, and robust fallback mechanisms that drove 2–3x page view growth and +5–6k users. Also built a voice-based interview platform orchestrating Deepgram + GPT-4 Mini + OpenAI TTS with FSM-driven reliability, and has hands-on RAG experience (LangChain, hybrid retrieval, cross-encoder reranking, custom pseudo-query generation).”
Rethvick Sriram Yugendra Babu
Junior AI/ML Software Engineer specializing in Generative AI and scalable data pipelines
“Built and operated large-scale biodiversity/ecological research platforms, integrating 50+ heterogeneous global datasets into a unified BIEN 3 schema on PostgreSQL/PostGIS and improving data consistency by 35%. Strong production engineering background (Linux monitoring, CI/CD performance gates, Docker on AWS/Azure) plus applied AI work building a Python RAG system (0.90 precision) and halving latency with Elasticsearch.”
MANOGNA VADLAMUDI
Junior AI Engineer specializing in LLM evaluation, prompt engineering, and AI orchestration
“LLM workflow builder who has deployed a personalized GPT experience (including Delphi AI-based knowledge ingestion) and built a LangChain/LangGraph job-aggregation pipeline that ingests, normalizes/dedupes, filters, then uses an LLM to rank and summarize matches. Emphasizes production reliability with structured outputs, retries/fallbacks, metric-driven evaluation, logging/prompt versioning, and A/B testing, and collaborates with non-technical stakeholders through demo-driven iteration.”
Zachary Ringwood
Junior Full-Stack Developer specializing in React Native and Java/Spring
“Frontend engineer who created an in-house React-like framework (“React-Wilcox”) enabling modern, event-driven UI components on extremely legacy browsers (as far back as 2002), including race-condition avoidance via batched state updates. Also does freelance work untangling AI/vibe-coded frontends for nontechnical founders, componentizing UIs and fixing routing/readability, and recently built a React+TS social app for martial artists with privacy-preserving location distance features.”
Varun Mahankali
Junior Full-Stack Software Engineer specializing in React, Node.js, AWS, and Generative AI
“Built and production-deployed a Streamlit-based PDF RAG chatbot using LangChain (FAISS, embeddings, prompt templates) and OpenAI, optimizing Streamlit’s stateless behavior by caching vector DB + chat history to cut latency and API cost. Demonstrates a rigorous evaluation mindset (gold datasets, unit tests, LLM-as-judge, groundedness KPIs) and has experience communicating privacy/accuracy safeguards (RBAC, data masking, citations) to a non-technical client at Kalven Technologies.”
Binaya Sharma
Senior Software Engineer specializing in full-stack systems, big data, and applied AI
“Built and deployed ForensicLLM, a local domain-specific LLaMA-3.1-8B model for digital forensic investigators using RAFT + RAG over 1000+ curated research papers, with citation-aware responses and rigorous evaluation (BERTScore/G-Eval). Deployed via vLLM and Docker and validated through a chatbot survey with 80+ participants; published at DFRWS EU 2025.”
Srikar Tharala
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
“Currently at ProShare and reports building an AI/LLM-powered system deployed to production, aimed at helping with status-related difficulties and reducing misunderstandings across transactions. Also cites prior collaboration at Porsche with marketing teams, focusing on translating marketing goals into technical requirements and communicating solutions clearly to non-technical stakeholders.”
Atharva Chavan
Junior Full-Stack Software Engineer specializing in mobile, cloud, and GenAI integration
“Software engineering intern with hands-on ownership of a Java/Spring Boot order management microservice, including production performance tuning via Redis caching and database indexing driven by API logs/metrics. Also contributed to a production mobile-backend LLM feature using RAG with embeddings over structured data and documents (DB + object storage), with guardrails to keep responses grounded.”
Avanishika Revelli
Junior Software Engineer specializing in backend, cloud, and robotics automation
“Graduate Research Assistant in Robotics at Arizona State University who built an end-to-end LLM-driven task execution framework enabling collaborative robots to convert high-level natural language instructions into safe, executable ROS actions. Implemented robust monitoring, failure detection, and automatic replanning, and addressed real-world issues like timestamp/frame-transform mismatches and heterogeneous robot interoperability using adapter nodes.”
Amr Elkordy
Mid-level Sales Engineer specializing in GNSS/RTK and technical pre-sales
“GEODNET engineer specializing in edge-to-cloud, real-time GNSS data pipelines at global scale (thousands of heterogeneous base stations). Built deterministic-latency ingestion with RTCM/MSM normalization, jitter buffering, and firmware-aware parsing, and shipped production hotfixes using canary rollouts and deep observability. Also delivers customer-specific GNSS/RTK outputs via tested Python tooling (CLI/API) and collaborates on-site with operators to resolve firmware and network-driven issues.”
Gomathy Selvamuthiah
Junior Data/AI Engineer specializing in MLOps, real-time pipelines, and LLM applications
“Built an LLM-driven MLOps agent at SBD Technologies that automated an EV-charging prediction workflow end-to-end, integrating with real-time Kafka/FastAPI systems supporting 120K+ chargers at 99.99% event delivery. Addressed frequent schema drift by implementing SQLAlchemy/Flyway validation (60% reduction in drift issues) and deployed as Kubernetes microservices with GitHub Actions CI/CD; also has Airflow-based ingestion/crawling experience into Snowflake and stakeholder-facing delivery via a Fleetcharge PWA.”
Mahgoub Gaafar
Senior Product Manager specializing in AI-driven engagement and gamification
“F2P product/game designer with live-ops experience on an NBC Group-owned mobile app in the MENA region (~200k users), driving personalization (segmented ads/trivia) and monetization (regional pricing, LTOs, season pass). Owns the full delivery lifecycle—PRDs/backlog through QA/UAT and release—and uses retention/conversion metrics and A/B testing to tune rewards and the game economy.”
Ram Abhinav Vedant Madabushi
Junior Full-Stack/AI Engineer specializing in web platforms and LLM applications
“Backend engineer from FoodSupply.ai who built and evolved a scalable restaurant/supplier product and order management platform using Node.js and REST APIs. Implemented a hybrid MySQL+MongoDB data architecture, optimized performance with Redis/Prisma, and led a phased migration with feature flags and a temporary sync layer to maintain data consistency. Strong focus on production security (OAuth2, RBAC, row-level security, AWS IAM) and reliability practices (testing with Pytest, Docker/AWS pipelines).”
FNU ASHUTOSH
Mid-level Software Engineer specializing in AI, full-stack systems, and FinTech
“Product-minded full-stack engineer with experience in fintech identity verification and industrial analytics, focused on turning repeated operational pain points into reusable platforms. Built real-time KYC/KYB dashboards, secure cross-platform web components, and a multi-tenant workflow engine that cut onboarding from 2 weeks to 1 day while materially improving conversion, reliability, and developer speed.”
Rohan Halder
Entry-level Account Executive with sales experience in SaaS and retail
“Sales candidate with SDR-to-AE progression who combines very high outbound activity levels (roughly 500 emails and 200+ calls per day) with consultative demos and objection handling. Reported generating about $197.98K in pipeline and uses a practical, data-informed approach to building pipeline from scratch by leveraging existing customer relationships, referrals, and vertical engagement signals.”
I Anuj
Junior Backend/Infrastructure Engineer specializing in AWS distributed systems
“Backend engineer with 1.7 years of experience plus prior founding experience who has already owned production systems end-to-end in an early-stage environment. Most notably, they rebuilt a failing ingestion pipeline into a stable SQS/Fargate architecture that improved success from 40% to 100%, boosted throughput 10x, and cut processing time by ~75%, while also shipping an LLM-powered fashion search workflow using Vertex AI and Elasticsearch.”
Atharva Bibave
Entry-level Full-Stack Software Engineer specializing in backend, cloud, and AI systems
“Software engineer with hands-on experience across platform modernization, production AI agents, and workflow automation. They led a monolith-to-microservices migration that increased deployment speed from weekly to daily, built a self-healing GPT-powered browser agent with an 85% autonomous recovery rate, and founded/ran ZapDash, where they hardened Kafka-based integrations against silent data loss.”
“Software/product engineer who has owned a consumer iOS dating app from customer discovery through roadmap execution, while also shipping an in-app LLM-powered support/feedback bot. Brings a mix of product sense and backend systems experience, including rebuilding a race-condition-prone event orchestration system and designing microservices to handle arbitrary black-box production data.”
Ayushi Modi
Entry-level Software Engineer specializing in full-stack and AI systems
“Software engineer with hands-on experience spanning backend APIs, streaming data systems, and cloud/infrastructure automation, who is already using agentic AI workflows in a disciplined way. Stands out for combining practical systems work in Spring Boot, Kafka/Spark/ClickHouse, and Terraform/Kubernetes with a thoughtful approach to AI oversight, architecture, and multi-agent orchestration.”
Shuchi Shah
Senior Applied AI Engineer specializing in RAG and full-stack systems
“Backend engineer with experience building an end-to-end civic tech AI platform that ingests city council meeting videos, transcribes them with Whisper, and enables natural-language Q&A via a LangChain/FAISS RAG pipeline. Demonstrated strong systems thinking by tuning retrieval for accuracy/latency/memory (cutting response time ~3s→1s and memory ~500MB→25MB) and by safely migrating an ERP from monolith toward services using dual writes, reconciliation, and idempotency to protect financial workflows.”
Luka Mgaloblishvili
Mid-level Software Engineer specializing in integrations and automation
“Union College robotics graduate with hands-on ROS experience building an independent “waiter bot” that took spoken orders (Mozilla DeepSpeech), used OpenCV, and navigated via SLAM on a TurtleBot with LiDAR. Also led the Union College Robotics crew for four years and implemented PID control for micromouse maze robots; has additional software deployment experience with Dockerized FastAPI and CI/CD from coursework.”
Darshan Shah
Mid-Level Software Engineer specializing in cloud-native microservices and full-stack development
“Full-stack engineer with deep startup experience building products from scratch under ambiguous requirements. Delivered a scalable, admin-configurable notification platform (Spring Boot/Java/Kafka) supporting 50+ notification types across 3 channels for 10k+ users, cutting new notification setup to ~5 minutes. Also built a Tinder-meets-LinkedIn job-swiping app (React/TS + Node/Prisma) and has hands-on AWS production ops (ECS/EKS, RDS, CloudWatch) plus multiple third-party integrations (Stripe, QuickBooks, Twilio).”
Gagan Jagadish
Junior AI/ML Software Engineer specializing in automation and healthcare imaging
“Backend-focused engineer who built a Python-based automation system leveraging Gemini AI and prompt-driven PDF field extraction to replace a previously manual third-party workflow. Drove stakeholder alignment around accuracy/acceptance thresholds and added production-minded safeguards like graceful failure handling and backup model contingencies.”