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Vetted Unit Testing Professionals
Pre-screened and vetted.
Vigneshwaran Moorthi
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and Clinical AI
“Built and productionized a HIPAA-compliant LLM+RAG Clinical AI assistant at Optum, fine-tuning GPT/LLaMA on de-identified patient notes and integrating FAISS/Pinecone for sub-second retrieval; reported to cut diagnosis time by ~20 minutes per case. Experienced in orchestrating ML pipelines (Airflow, AWS Step Functions, Azure Data Factory) and in reliability techniques for LLM systems (grounding, citations, confidence filters, monitoring) while partnering closely with clinicians and compliance teams.”
Hans Walker
Junior Machine Learning Engineer specializing in generative AI and computer vision
“AI engineer who deployed a production LLM-powered safety system for an education platform, combining rule-based checks, multi-LLM verification, and selective context (prompt+image vs image-only) to prevent explicit prompts/images from getting through. Strong focus on reliability via benchmarking, trace-based failure analysis, and continuous improvement driven by stakeholder feedback and manual review.”
Kumud Sharma
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and AI integrations
“Backend engineer who has delivered large, measurable performance wins (10x throughput, 67% latency reduction) by combining Flask microservices, Redis caching, and AWS autoscaling/observability. Has hands-on depth in SQLAlchemy/Postgres optimization and production scaling pitfalls (cache consistency, connection exhaustion), plus experience deploying real-time ML inference (XGBoost) on AWS Lambda and building secure multi-tenant Kubernetes isolation.”
ASHWINKUMAR PACHIPALA
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
“Full-stack Java developer with IBM and Epic Systems experience modernizing legacy enterprise apps into microservices and delivering customer-facing healthcare claims workflows at very high scale (2M+ transactions/day). Strong blend of product engineering (APIs + React/TypeScript UI) and production operations on AWS, including performance incident remediation via query optimization, indexing, and autoscaling.”
Saketh Reddy
Mid-Level Software Development Engineer specializing in full-stack and LLM/AI systems
“AI engineer with hands-on production experience building an end-to-end RAG system that reduced document-answering time from hours to minutes, improving accuracy through chunk overlap and hybrid BM25+semantic retrieval. Also built a LangGraph-based agent that researches company financial news via web search (Google Serper), using Pydantic structured outputs and checkpointing for reliability; experienced collaborating with non-technical stakeholders at JPMC and communicating ROI.”
Chandana reddy
Mid-level Full-Stack Developer specializing in cloud-native microservices and distributed systems
“Software engineer with hands-on ownership of both fintech checkout improvements (saved payment methods/one-click checkout with tokenization and feature-flag rollouts) and production LLM/RAG systems for customer support. Demonstrates strong operational rigor via guardrails, evaluation loops integrated into CI/CD, and scalable data pipelines handling messy PDFs/CSVs/logs with reliability and observability.”
Kunal Santosh Bhujbal
Mid-level Full-Stack Software Engineer specializing in AI platforms and cloud microservices
“Distributed-systems engineer applying robotics-style patterns to software: built "Vibecheck," a high-throughput real-time video + OS-telemetry fusion and analysis system (500+ MB/session) with strict latency constraints. Strong in containerization and CI/CD (Docker, GitHub Actions) and in designing fault-tolerant, event-driven architectures (Kafka/RabbitMQ), plus hands-on debugging of multi-agent coordination using blackboard + watchdog/circuit-breaker control patterns.”
Harshini Jaligama
Senior Full-Stack Java Developer specializing in cloud-native microservices and FinTech
“Full-stack engineer (5+ years with Java/Spring Boot and React) who has built and deployed AWS-based microservices platforms using Kafka for real-time rewards/promotions and large-scale telemetry analytics. Demonstrates hands-on scalability expertise (partitioning, consumer groups, durability/acks, idempotency) and production-minded delivery practices (CI/CD, Docker, testing, Swagger, monitoring).”
Jaideep bommidi
Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps
“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”
Bryan West
Senior Software Engineer specializing in AI, cloud infrastructure, and full-stack development
“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”
Goutami Amara
Mid-level Full-Stack Java Developer specializing in FinTech and Healthcare platforms
“Software engineer who built internal operations/monitoring dashboards for real-time trading and money-movement systems, emphasizing auditability and rapid iteration. Deep experience with microservices on Azure using Kafka/RabbitMQ, plus strong testing discipline (JUnit/Mockito/Testcontainers, contract/E2E) and observability patterns (correlation IDs, centralized logging, distributed tracing) to reduce incident triage time and improve resilience.”
Sai Harshith Tanguturi
Mid-Level Software Engineer specializing in Java microservices and AWS cloud-native systems
“Full-stack engineer who has owned customer-critical analytics and course intelligence platforms end-to-end (React/TypeScript + Node/Express + SQL), including an internal self-serve Reporting & Analytics Center adopted by 1,000+ users. Demonstrates strong systems thinking across performance (2× faster heavy reports), reliability (feature flags, testing), and distributed architecture (RabbitMQ microservices with idempotency, DLQs, and correlation-ID observability).”
Narendhiran Saravanane
Junior Robotics Software Engineer specializing in ROS 2, controls, and applied AI
“Robotics software engineer with 2+ years across ROS1/ROS2 projects spanning humanoid behavior engines and agricultural robots. Built an LLM-driven, ROS2-lifecycle-based decision system plus micro-ROS firmware on Teensy for modular sensors/motors, adding health monitoring that improved reliability 10x. Strong simulation/testing and deployment discipline (Gazebo, 95% coverage, Docker + AWS Greengrass/ECR, CI/CD) and demonstrated localization expertise with EKF sensor fusion achieving <0.5% error.”
Aditya Agrawal
Intern Software Engineer specializing in backend systems and data engineering
“Backend/AI engineer who has built and shipped two products: Know Founder (Python/SQL/AWS) scaling to 2,000+ users in the first month, and Unifr (unifr.online), an AI search visibility engine that queries multiple LLMs and turns responses into structured brand insights. Strong in production reliability/performance (Redis caching, indexing, precomputation) and in designing agentic workflows with guardrails, validation, retries, and human escalation.”
Rasika Deodhar
Mid-level Full-Stack Software Engineer specializing in Generative AI
“Full-stack engineer who shipped an end-to-end speech capability for an LLM chatbot UI, integrating OpenAI APIs and publishing via Google Apigee with client documentation. Has experience operating deployments with Jenkins/Kubernetes/Docker and monitoring with Datadog, and has worked in an innovation-center environment building rapid prototypes under ambiguity with tight stakeholder feedback loops.”
Jeetendra Girish Vasisht
Junior Software Engineer specializing in cloud-native microservices
“Backend engineer (Nokia) who designs and migrates cloud-native microservices at scale, including a secure low-latency system handling 500k+ daily transactions. Strong in Kubernetes/OpenShift operations, CI/CD standardization, and production security (OAuth2/JWT/RBAC) with SOC2-aligned controls and zero critical security incidents. Demonstrated expertise in safe migrations (canary/blue-green, dual writes, reconciliation) and concurrency correctness in real-time systems.”
Szymon Kościelniak
Senior Unity Developer specializing in mobile and VR/PC games
“Unity gameplay engineer from Moonactive who rewrote the gameplay engine for ZenMatch, maintaining two engines in parallel via a separation layer to improve robustness/scalability and make new content easier to ship. Also implemented multiplayer for a client prototype using the Onion networking framework and is familiar with Photon Fusion concepts.”
Sai Chatrathi
Mid-level AI/ML Engineer specializing in healthcare analytics and MLOps
“Built and deployed a production LLM-powered lesson adaptation platform for K–12 educators that personalizes content for multilingual and neurodiverse students using RAG and content transformation. Owned the full stack from FastAPI backend and OpenAI integration through reliability/safety controls, latency/cost optimization, and weekly shippable modular APIs, iterating directly with curriculum stakeholders to reduce hallucinations and improve educator trust.”
Hetvi Shah
Mid-Level Full-Stack Software Developer specializing in React/Angular and Node.js
“Frontend lead who owned architecture and quality for TELUS’s Next Generation Sales Platform, building a modular React+TypeScript experience that scaled across wireline/wireless products and channels. Experienced in hardening UIs against unreliable backend integrations (API abstraction, retries/fallbacks, caching, logging) and delivering real-time dashboards via WebSockets, with strong CI/CD testing and blue-green release practices for high-stakes launches like Black Friday.”
Binaal Bopanna
Mid-level Solutions Engineer specializing in AI automation and hybrid cloud infrastructure
“Built and productionized AI-driven insurance claims document intelligence/automation at American Family Insurance, integrating OCR/NLP models and a rules-based validation layer into existing claims systems via APIs. Delivered measurable impact (≈28% accuracy lift, ≈35% reduction in manual processing time) and modernized legacy workflows with phased cloud migration, feature flags, parallel runs, and CloudWatch-based monitoring.”
Harshitha Parupalli
Mid-level Data Engineer specializing in multi-cloud real-time and batch data pipelines
“Data engineer with healthcare domain experience who owned 100M+ record pipelines end-to-end (Kafka/Kinesis/ADF → PySpark/dbt validation → Spark SQL transforms → Snowflake/Power BI serving). Built production-grade reliability practices (Airflow orchestration, CloudWatch/Grafana monitoring, pytest + contract/regression tests, idempotent ingestion/backfills) and delivered measurable improvements: 35% lower latency and 40% better query performance.”
Austin Patten
Senior Unity Engineer specializing in mobile games and interactive apps
“Lead engineer at PeopleFun who owned a data-driven limited-time event system for Wordscapes Search, using Addressables and asset bundles to ship new live content without app updates. Their work was tied to improved retention and stronger player engagement, and they also bring pragmatic AI-assisted development practices plus early VR/Meta Quest prototyping experience.”
SAITEJA MALLEMPUDI
Senior Data Scientist and AI/ML Engineer specializing in GenAI and cloud ML
“ML/AI engineer with hands-on experience owning systems from experimentation through deployment and monitoring, including a Bank of Montreal project that improved timely interventions by 12%. Also brings GenAI/RAG experience with evaluation and safety guardrails, plus clinical NLP pipeline work extracting medication data from notes for patient risk prediction.”