Vetted Engineering Professionals in the Greater Phoenix

“Backend/platform engineer with strong ownership of production systems, including a full Azure migration from a VM-based monolith to a containerized, event-driven microservices architecture. They combine cloud infrastructure, LLM/RAG optimization, and pragmatic stakeholder management, with measurable wins including 90% infra cost reduction, faster deployments, and significantly improved latency and token efficiency.”

Thanmayi Ravuri

Screened

Entry Software Engineer specializing in cloud backend and microservices

Tempe, AZ1y exp

“Built production-oriented LLM agent systems for incident investigation and CRM workflows using LangGraph, FastAPI, AWS, and retrieval grounding. Stands out for treating agents like real software systems—adding schema enforcement, retries, fallbacks, monitoring, and eval loops—and tying that work to measurable gains in accuracy, latency, and analysis speed.”

Mohammed Mudassir Hussain Shaikh

Screened

Entry-Level Full-Stack Software Engineer specializing in serverless AWS and AI applications

Tempe, AZ1y exp

“Built and deployed serverless AWS applications (Lambda/S3/RDS Proxy) including a NASA L’Space React + Python data analysis tool, focusing on performance for large datasets. Demonstrates strong cloud troubleshooting across compute and networking (CloudWatch-driven diagnosis, EC2 scaling, security group fixes) and a user-driven iteration loop that improved product usability with dynamic filtering and interactive UI.”

“Mechanical/manufacturing professional (not a software/Next.js candidate) who emphasizes applying Lean/Six Sigma-style manufacturing quality and continuous improvement tools (Kaizen, PFMEA/DFMEA, DOE, DMAIC, root cause analysis). Target base salary stated as 130,000.”

“AWS-focused backend/data engineer with hands-on production experience building serverless APIs (Lambda/API Gateway) secured with Cognito/JWT, deploying via Terraform + CI/CD, and managing secrets with Secrets Manager/Parameter Store. Also built AWS Glue ETL from S3 to RDS with schema evolution and data-quality controls, modernized a monolith into microservices using parallel testing, and delivered major SQL performance gains (minutes to seconds) while owning incident response for batch pipelines.”

“Backend engineer specializing in event-driven/serverless systems and Python/FastAPI APIs. Built a scalable PDF-to-structured-data pipeline on AWS (S3, Lambda, Step Functions, Textract, DynamoDB, SNS) with strong observability (p50/p90/p99) and reliability patterns (idempotency, retries/DLQs), and has led zero-downtime migrations using feature flags, dual writes, and incremental rollouts.”

Mayank VYAS

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG retrieval, and IoT ML systems

Tempe, AZ4y exp

“Built production LLM-driven products including a job-hunt AI (job ranking + resume optimization) and an InterviewAI agentic pipeline using LangChain. Focused on practical deployment concerns like securing OpenAI usage via rate limiting and tiered quotas, and demonstrates an applied approach to choosing models, retrieval methods (RAG), and prompting strategies.”

“Backend engineer who has led redesigns and migrations for a real-time logistics platform, improving scalability and resilience while managing eventual consistency tradeoffs. Demonstrates strong distributed-systems rigor (idempotency, transactions, async queues, monitoring) and builds secure, versioned FastAPI APIs with JWT/OAuth2, RBAC, and database row-level security.”

Jeevan Hebbal Manjunath

Screened

Junior Robotics & Machine Learning Engineer specializing in autonomous systems

Tempe, AZ2y exp

“Robotics engineer leading development of a Physical Reservoir Computing controller for a pneumatic soft robotic arm, owning everything from automated data collection and leak-testing automation to hardware design/manufacturing and cross-lab integration with Virginia Tech. Built ROS 2/DDS-based multi-robot systems integrating OptiTrack, a lab quadruped, and a UR5e, and pairs simulation (Gazebo/MuJoCo) + PPO RL training with production-ready tooling (Docker, CI/CD, Flask dashboards, RAG chatbot portfolio).”