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Vetted CI/CD Professionals
Pre-screened and vetted.
Bhavana Anna
Mid-level AI/ML Engineer specializing in fraud detection and Generative AI (RAG)
“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”
Nicholas Kim
Entry-Level Software Engineer specializing in full-stack and backend development
“Full-stack developer who built a workout tracker feature end-to-end on the PERN stack (Postgres/Express/React), including relational schema design, REST APIs, and optimistic UI updates. Also has Next.js App Router experience (dynamic routes, SSG/React Server Components) and a strong quality mindset from Boston Scientific, where they used TDD to support clinically sound ECG analysis software and drove backend test coverage to 97%.”
Bilal Ahmed
Senior Linux Systems Engineer specializing in hybrid cloud and DevOps automation
“Cloud/infrastructure engineer from ASM Research supporting federal healthcare systems, operating multi-cloud (AWS/Azure/GCP) environments at ~2000-server scale. Deep hands-on experience with Terraform/Ansible IaC, PR-based governance (Atlantis), and secure CI/CD (OIDC/least privilege), with concrete incident response wins and HA/failover testing improvements. Not an IBM Power/AIX specialist but comfortable translating virtualization/partitioning and ops practices to new platforms.”
Sai Erramada
Mid-level Full-Stack Java Developer specializing in microservices and cloud-native systems
“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.”
Phanideep P
Senior Data Engineer specializing in cloud lakehouse and streaming data platforms
“Data platform/data engineer with cross-industry experience in banking and healthcare, building cloud-native lakehouse architectures across AWS/Azure/GCP. Has owned high-volume (millions of records; TB/day) pipelines with strong data quality automation (dbt/Great Expectations), observability (Grafana/Prometheus), and real-time streaming (Kafka/Spark) for fraud monitoring; also delivered an early-stage migration from SQL Server to BigQuery with 40% batch latency reduction.”
Sandipkumar Prajapati
Mid-Level Software Engineer specializing in full-stack systems and authentication
“Full-stack engineer who led production modernization of a legacy, latency-sensitive application into a React + microservices platform, with heavy TypeScript backend work to improve reliability and maintainability. Has operated and scaled authentication/identity services in production, addressing peak-traffic latency spikes via database tuning and improved observability, and emphasizes idempotent, retry-safe workflow design.”
Rahul Ganesan
Intern AI Engineer specializing in LLM systems, RAG, and cloud data pipelines
“Built and deployed a production Dockerized multimodal (voice+text) LLM agent for knowledge management that retrieves from Notion and documents and falls back to Tavily-powered web search with citations when internal notes are missing. Emphasizes production reliability via model-switching fallbacks, caching, strict structured outputs (Pydantic/JSON schema), and MCP-based orchestration with state-aware gating and monitoring to reduce redundant tool calls and improve success rates.”
Aniruddha Chakravarty
Junior Software Engineer specializing in cloud infrastructure, observability, and full-stack systems
“Built and productionized a predictive maintenance system (predictEngineLife) estimating Remaining Useful Life for PW4000 turbofan engines from large-scale, noisy telemetry—emphasizing modular pipeline design, deterministic preprocessing, and strong observability/guardrails. Also has hands-on experience diagnosing multi-agent LLM customer-support workflows (schema/state issues, fallback paths, regression tests) and has led developer workshops (GDG Pune) while partnering with sales teams on technical discovery and POCs.”
Sai Bandaru
Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems
“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”
Sai Leela Kuragayala
Mid-level Full-Stack Software Engineer specializing in scalable web apps and automation
“UE5 UI engineer who has shipped production-ready HUD/menu frameworks using C++/Slate/UMG and CommonUI, emphasizing MVVM-style architecture for maintainability and designer-friendly iteration. Strong in UI profiling/optimization (Unreal Insights + Slate Profiler), including Slate list virtualization and event-driven updates that improved UI frame time by ~30% in heavy menu scenarios.”
Srinandh Reddy
Mid-Level Software Engineer specializing in backend, cloud, and event-driven systems
“Robotics software engineer focused on backend and distributed systems for real-time robot operations, including sensor ingestion, robot state management, and robot-to-cloud communication. Hands-on with ROS/ROS2 integration and real-time navigation debugging, plus production-grade monitoring, CI/CD, and containerized deployments (Docker/Kubernetes) to improve stability and performance.”
Sukesh Sadhu
Mid-Level Software Engineer specializing in FinTech payments and event-driven microservices
“Backend/data engineer focused on fintech payments and fraud systems, owning real-time Kafka-based reconciliation pipelines end-to-end (~13k tx/day). Built audit-friendly validation/reconciliation (SQL + Python), kept lag to seconds, and cut errors ~20%, while also shipping Spring Boot APIs with Redis caching and strong idempotency/versioning. Has early-stage startup experience standing up payment services on AWS with Docker + GitHub Actions and production monitoring/incident handling.”
Meghana Reddy Devireddy
Senior DevSecOps Engineer specializing in Azure cloud infrastructure and CI/CD
“GCP-focused database/infrastructure engineer with hands-on production support for Cloud SQL and Firestore, spanning provisioning, IAM, scaling, backups, and performance tuning. They also described supporting a hybrid GCP architecture for a monolithic on-prem PostgreSQL workload and resolving a major latency incident by tracing cascading failures and fixing indexing issues.”
Kuldip Savaliya
Mid Software Engineer specializing in AI automation and full-stack systems
“Built and shipped a production LLM-powered email automation agent for procurement that ingests emails/attachments, classifies requests via rules+embeddings+LLM fallback, enriches responses with SAP inventory data, and generates templated replies. Architected it as an event-driven, idempotent Azure Functions/Queues pipeline with schema-constrained outputs, confidence gating, retries/circuit breakers, and Application Insights monitoring—cutting turnaround time from 4–7 days to near real-time while maintaining zero downtime.”
Kush Shah
Junior Full-Stack Software Engineer specializing in web apps and automation
“Backend engineer with hands-on experience building an AI-powered document processing pipeline for insurance workflows from design through deployment and production support. They combine LLM-based extraction with rule-based validation, retries, and observability, showing a pragmatic approach to making AI systems reliable in high-stakes environments.”
Anvith Reddy T
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”
Chin-yu Wu
Junior Data Analyst specializing in sports analytics and business intelligence
“Analytics professional in the sports industry who has owned high-impact revenue and compliance data projects for the Colts, turning fragmented Ticketmaster and Salesforce data into trusted real-time reporting. Stands out for combining strong SQL/Snowflake engineering, rigorous validation practices, and stakeholder-facing metric design that drove a record 98% compliance rate and meaningful revenue recovery.”
Amarnath Kaushik
Junior Software Engineer specializing in full-stack systems and AI applications
“Full-stack AI engineer who has owned production deployments for both a voice journaling/emotional insights app and a RAG-based research assistant. Stands out for turning messy, failure-prone LLM and document pipelines into reliable user-facing systems through strong debugging, staged workflow design, and post-launch stabilization.”
Pranathi Nallala
Mid-level AI Engineer specializing in distributed systems and LLM applications
“Built production AI agents that convert natural-language requests into structured workflows using LangChain, tool calling, and a Kafka/Kubernetes backend, with strong emphasis on tracing, validation, and self-correcting failure handling. Also drove a zero-to-one Research Day judging platform spanning React, Flask, RAG, and ILP-based assignment optimization for ~100 live posters, achieving 99% uptime and winning Best Web App.”
Deepak Singh
Mid Software Engineer specializing in systems, CI/CD, and applied machine learning
“Engineer at Syniti who uses AI tools pragmatically to speed development while maintaining quality through rigorous validation, code reviews, and CI/CD. Most notably, they leveraged AI-assisted testing to increase test coverage from 10% to 70%, and they are actively exploring more advanced agent-based development workflows.”
Sakshi Patel
Mid-level Software Engineer specializing in e-commerce and supply chain platforms
“AI-focused developer who has built several practical AI products, including EchoMate, a voice-agent system designed to act as a proxy for doctors and support patients when physicians are unavailable. Also has experience with multi-agent/API-based workflows in a solar suitability project, showing interest in applying AI across both healthcare and climate-related use cases.”
George Platon
Principal Full-Stack Engineer specializing in AI, DevOps, and cloud platforms
“Built a production end-to-end AI video-to-reels clip extraction system using a multi-agent architecture with transcription, captioning, effects generation, and centralized orchestration. Demonstrates unusually strong systems thinking around reliability, observability, evaluation, and production tradeoffs for LLM-powered workflows, including Kubernetes/Kafka-based deployment and regression-driven prompt governance.”
Atharva Rajesh Patil
Junior Software Engineer specializing in backend systems and cloud-native applications
“Engineer with hands-on experience owning customer deployments for ordering and billing systems at Amdocs, including performance tuning, CI/CD improvements, and post-launch stabilization that delivered about 50% faster execution time. Also built and debugged an LLM-powered task prioritization app using Gemini, Streamlit, Python, and MongoDB, with a strong focus on prompt reliability, validation, and handling inconsistent real-world inputs.”
Kevin Delong
Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”