Vetted Mockito Professionals

Preethi Bandari

Screened ReferencesStrong rec.

Mid-level Full-Stack Developer specializing in scalable web applications

Memphis, TN5y exp

“Developer who uses AI tools pragmatically to accelerate coding while keeping full ownership of system design and decision-making. Emphasizes rigorous review, testing, and alignment with architecture, security, and performance standards, and stays current on AI through both industry sources and hands-on experimentation.”

“Backend-focused engineer who stepped into a brand-new product and built most of a Go-based backend from scratch in a 3-7 person team, initially as the only backend developer. He designed core security and access-control systems like MFA and role-based authorization, owned complex workflow features such as seat licensing, and helped deliver a B2B SaaS dashboard that earned very positive client feedback.”

“Backend/platform engineer in healthcare and consulting (Molina Healthcare, TCS) who productionized real-time eligibility/authorization and care navigation workflows with strong reliability and HIPAA security. Demonstrated measurable performance gains (≈40% latency reduction, ~99% uptime) using Spring Boot APIs, SQS decoupling, Redis caching, and deep observability, and regularly leads technical demos that accelerate client adoption.”

“Backend engineer from insurance/enterprise platforms with a focus on high-performance, real-time, event-driven microservices. Brings strong distributed-systems communication design plus mature testing, Docker containerization, and CI/CD practices; no direct robotics/ROS experience.”

“Full-stack + ML engineer who built “Finsight,” a real-time financial risk platform (React/FastAPI/MongoDB/AWS Lambda) processing 2M+ records monthly, using sharding and Redis caching (60% DB load reduction) plus async and batch optimizations. Also has healthcare product experience at Apollo Healthcare, partnering directly with clinicians/admins to design and iterate EHR dashboards via Figma prototyping and user testing, and demonstrates clear system design thinking for real-time voice-to-LLM architectures.”

“Backend engineer with experience in both healthcare (Siemens) and payments (Bitwise), focused on scaling Python APIs and modernizing architectures. Has led monolith-to-microservices migrations and introduced Kafka async processing, Redis caching, and ELK observability, citing ~40% faster issue resolution and improved reliability via idempotency and strong security controls (OAuth2/JWT, RBAC, RLS).”

“Customer-facing software engineer who rapidly turns business requirements into Figma prototypes and PoC applications, using workflow prioritization and frequent client reviews to stay aligned. Has hands-on experience integrating with existing authentication/user APIs, building MongoDB-backed caching, and implementing robust fallback/retry mechanisms. Comfortable working on-site with customers and resolving production issues in AWS (e.g., DNS/EC2 traffic routing) in collaboration with DevOps.”

“Master’s-program backend engineer with strong Java/Spring Boot industry experience who also owned a Python analytics service (Flask/Postgres, JWT, Celery/Redis) and optimized large-dataset performance via SQL/batching. Has hands-on Kubernetes microservices deployment and GitLab+Terraform CI/CD/GitOps workflows, plus experience supporting phased on-prem to AWS migrations and building Kafka-based real-time streaming pipelines.”

“Robotics-focused full-stack engineer with hands-on ROS experience building sensor-processing and control nodes, plus a track record of debugging and optimizing real-time robot responsiveness via profiling and message-timing analysis. Uses Webots for pre-hardware validation and Docker/CI/CD to standardize deployments and catch issues early.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

“Built an LLM-powered agent within the Nora AI analytics platform to automate e-commerce product performance analysis and generate actionable recommendations (pricing/inventory), designed with production-grade reliability patterns and observability. Emphasizes predictable, schema-validated tool/function-calling pipelines with robust fallbacks, idempotency, and guardrails for messy operational data.”

Harshini Jonnala

Screened

Senior Backend Software Engineer specializing in distributed systems and cloud microservices

Hyderabad, India2y exp

“Backend engineer with NTT Data experience building Java/Spring Boot services for product-data ingestion, including Kafka-based asynchronous pipelines and Redis read-through caching. Also built a personal RAG system deployed on Google Kubernetes Service using FastAPI, LangChain, and Pinecone with multi-tenant data isolation; holds a Master’s background in Machine Learning.”

Mohana pavan Vankayala

Screened

Mid-level Java Full-Stack Developer specializing in microservices and AWS

Overland Park, KS4y exp

“Full-stack engineer (HCL Tech) with 4 years building enterprise, high-throughput microservices on AWS/Azure using Java/Spring Boot and React. Demonstrated measurable performance gains (40% throughput) through Redis caching, deep SQL/query tuning, and Kafka-based async refactors, plus strong DevOps/observability practices with Jenkins/CloudFormation and Datadog/Splunk.”

Sai Erramada

Screened

Mid-level Full-Stack Java Developer specializing in microservices and cloud-native systems

Wisconsin, USA6y exp

“Backend engineer with hands-on experience building real-time, event-driven systems at Walgreens, including a Kafka-based prescription status notification service and scalable pipelines for messy prescription/inventory data. Strong focus on reliability patterns (retries, idempotency, DLQs) and iterating based on pharmacist feedback to improve usability.”