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Vetted Microservices Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in frontend architecture and scalable web platforms
Senior Full-Stack Engineer specializing in React, Python, and AI-driven SaaS
Senior Software Engineer specializing in FinTech and digital banking
Principal Data Engineer specializing in data pipelines and analytics systems
Senior Python Backend Engineer specializing in scalable APIs, cloud microservices, and AI/ML platforms
Senior AI/Software Engineer specializing in cloud security and AI-powered applications
Principal Software Engineer & Product Leader specializing in distributed systems and agentic platforms
Senior Full-Stack Python Engineer specializing in secure cloud platforms and ML systems
Staff/Lead Full-Stack Software Engineer specializing in .NET, Angular, and cloud architecture
Senior Full-Stack Engineer specializing in Clojure, AWS, and scalable web APIs
Principal Infrastructure Engineer specializing in distributed systems, cryptography, and hardware security
Senior Full-Stack Engineer specializing in SaaS, LegalTech, and Web3
Senior Full-Stack Engineer specializing in Python web applications
Naveen Kancharla
Mid-level AI & Backend Engineer specializing in RAG systems and scalable APIs
“Built and deployed a production LLM-powered document Q&A system using a strict RAG pipeline (LangChain-style orchestration + FAISS) to help users query large internal document sets. Demonstrates strong reliability focus through hallucination mitigation, curated offline evaluation with grounding checks, and production monitoring (latency/fallback rates) plus stakeholder alignment via demos and business metrics.”
Kruti Mehta
Mid-level Software Engineer specializing in backend systems and FinTech analytics
“Engineer with a pragmatic, high-leverage approach to AI-assisted development: uses AI and multi-agent workflows aggressively for implementation and internal tooling, while maintaining strict human oversight for user-facing features. Stands out for treating agents like junior engineers, breaking work into actionable tasks, and combining robust testing, local E2E validation, and feature-flag rollouts to safely ship production code.”
sathwik kuchana
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG on AWS
“Built and deployed an LLM-powered clinical decision support and risk monitoring platform for mental health at Valuai.io, emphasizing low-latency, evidence-grounded responses and crisis-safe behavior with clinician escalation. Strong production agent-orchestration background (LangChain/CrewAI) plus rigorous evaluation (clinician-in-the-loop + evaluator agent) and large-scale synthetic testing; also applied multi-agent workflows to document verification and fraud detection during an AI internship at Nixacom.”
Vikesh Patel
Senior AI/ML Engineer & Data Scientist specializing in LLMs, RAG, and MLOps
“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”
Phat Dang
Junior Software Engineer specializing in full-stack, DevOps, and GenAI
“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”
Saivedant Hava
Entry AI Engineer specializing in LLMs, RAG, and MLOps
“Built and shipped a production Python-based agentic RAG document retrieval system over 80K records using FastAPI, OCR, vector search, and AWS infrastructure, with a strong emphasis on reliability, testing, and observability. Stands out for treating AI failures like production incidents—turning hallucinations, retrieval misses, and OCR issues into regression tests—and for quantifiably reducing document lookup time from about 12 minutes to under 90 seconds.”