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Vetted Amazon DynamoDB Professionals
Pre-screened and vetted.
Intern-level Software Engineer specializing in full-stack and cloud systems
Mid-level DevOps & Platform Engineer specializing in Kubernetes and AWS infrastructure
Senior Full-Stack Python Developer specializing in Django, FastAPI, and cloud platforms
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
Director-level Engineering Leader specializing in cloud platforms, AI/ML, and scalable SaaS
Senior Full-Stack Developer specializing in cloud-native FinTech microservices and React
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
Mid-level Full-Stack Developer specializing in cloud-native microservices
Principal Automation Architect specializing in cloud DevOps, microservices, and MLOps
Mid-level Java Full-Stack Developer specializing in cloud-native microservices
Senior Full-Stack Java Developer specializing in Spring Boot microservices and cloud platforms
Senior Full-Stack Java Engineer specializing in microservices, cloud, and enterprise platforms
Senior Full-Stack Developer specializing in cloud microservices and enterprise applications
Aniruddha Rajnekar
Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems
“Full-stack engineer with about 3 years of experience who is deeply hands-on with AI-assisted development and agentic systems. Built TubeAgent using LangChain, Ollama, FAISS, and Llama 3, and has demonstrated measurable impact by cutting review time by 90% and reducing deployment time from 30 minutes to under 5 minutes at NC State. Combines practical experimentation with strong architectural thinking around resilient, composable AI systems.”
Harika N
Mid-level Software Engineer specializing in VR simulation and full-stack development
“Built and owned core systems for VISTA, a VR drone training simulator in Unity/C#, including modular training scenarios, drone physics, restricted airspace logic, and dynamic weather-aware gameplay. Stands out for combining VR performance discipline on Meta Quest with realistic-yet-trainable flight controls and close collaboration with faculty SMEs to align the simulation with real training workflows.”
Bill Cheng
Director-level Engineering Leader specializing in SaaS platforms and AI systems
“Entrepreneurial candidate building an LLC focused on applying AI to improve call center customer service, with an early go-to-market focus on local government call centers. They are already in discussions with a government prospect and have a clear thesis around solving high turnover and low knowledge retention through AI-assisted training and support systems.”
Swaraj Bhalerao
Mid-level Full-Stack Developer specializing in backend-heavy web applications
“Backend/full-stack engineer who has built AI-powered search and workflow systems in production, including a semantic resume-matching platform for recruiters and internal security data dashboards at ReliaQuest. Stands out for combining modern AI tooling with pragmatic reliability, performance tuning, and strong product intuition in ambiguous environments.”
JaswanthRaj Bantu Aindla
Mid-level Python Developer specializing in cloud-native microservices for FinTech and Insurance
“Backend/data engineer who has maintained high-traffic FastAPI microservices and delivered a hybrid AWS serverless+containers platform using Terraform and GitHub Actions, with secrets managed via Secrets Manager/SSM. Also led modernization of a mission-critical 10,000+ line SAS financial reporting engine into Python microservices and built AWS Glue ETL pipelines feeding a centralized data lake.”
Mohana Satyanarayana Moganti
Mid-Level Software Engineer specializing in Cloud Infrastructure and Full-Stack Platforms
“Built and shipped a production LLM-powered grading platform that automates rubric-aligned scoring and feedback, with strong guardrails (RAG grounding, structured JSON, validation/retries) and operational rigor (metrics, drift monitoring). Experienced using CrewAI to orchestrate multi-agent workflows end-to-end and validating quality via gold-set benchmarking against human graders with regression testing on every prompt/model change.”