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Vetted Unit Testing Professionals
Pre-screened and vetted.
Mid-Level Backend Software Engineer specializing in FinTech and payments
Mid-level AI Software Engineer specializing in LLMs, NLP, and MLOps for healthcare
Mid-level AI Software Engineer specializing in healthcare and agentic systems
Executive technology leader and Fractional CTO specializing in software architecture and AI automation
Staff Full-Stack Engineer specializing in AI agents and cloud platforms
Mid-level Full-Stack Engineer specializing in AI agent infrastructure
Mid-level Full-Stack .NET Developer specializing in Angular, Azure, and AI integrations
Senior Full-Stack Engineer specializing in web, mobile, and cloud applications
Senior Full-Stack Software Engineer specializing in cloud-native FinTech and data pipelines
Senior .NET Developer specializing in cloud-native microservices and modern web apps
Senior Full-Stack Python Developer specializing in microservices, data engineering, and cloud
Senior Full-Stack Developer specializing in Python, Django, React, and cloud-native SaaS
Mid-level Frontend/MERN Stack Developer specializing in scalable web applications
Mid-Level MERN Stack Developer specializing in full-stack web applications
Jelson Chacko
Senior Full-Stack Software Developer specializing in web applications and DevOps
“Frontend engineer who led a networking-device simulation PoC into a production-integrated product, designing microservice-style containerized architecture and optimizing massive telemetry streams with WebSockets and Kubernetes-based scaling. Built a React+TypeScript network orchestration dashboard (Platina Command Center) and drove quality/performance through unit/E2E testing with measurable browser/API performance metrics and CI/CD automation for rapid monthly releases.”
Mid-level Full-Stack Engineer specializing in cloud-native Java microservices
“Software engineer using AI pragmatically to accelerate development while keeping human review central to quality. Has hands-on experience applying AI and lightweight multi-agent workflows in a microservices environment spanning Java Spring Boot APIs, React modules, and Kafka event flows, with strong emphasis on architecture validation and production safeguards.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Mark Wlodawski
Senior Unity Developer specializing in AI/LLM systems and multiplayer VR
“Backend/data engineer focused on AWS-native Python systems: built a FastAPI microservice on ECS/Fargate serving real-time analytics at millions of daily requests with strong reliability (OAuth2/JWT, retries/timeouts, correlation IDs) and autoscaling. Also delivered Glue/PySpark ETL pipelines to curated S3 Parquet/Athena with schema evolution + data quality controls, owned Airflow pipeline incidents, and has a track record of measurable performance and cost optimizations (e.g., ~80%+ query latency reduction; reduced logging/NAT/Fargate spend).”
Amit Prajapati
Mid-level Software Engineer specializing in full-stack development and applied AI
“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”