Vetted CI/CD Professionals

Sreeja Venkatakrishnan

Screened

Mid-level Full-Stack Developer specializing in Java/Spring and modern JavaScript frameworks

Virginia, USA4y exp

“Full-stack engineer with hands-on experience building real-time applications (Socket.io chat app) and data-heavy systems in banking/loan management. Comfortable across React and backend services (Spring Boot/Node), with a focus on scalable API design, database performance (indexing/pagination/caching), and deployment via CI/CD and cloud infrastructure.”

Siddhartha Alapati

Screened

Mid-Level Software Engineer specializing in Java/Spring microservices and full-stack web apps

Denton, TX4y exp

“Software/full-stack engineer focused on deploying and integrating microservice applications into production AWS and hybrid cloud/on-prem industrial environments. Demonstrated end-to-end troubleshooting by tracing intermittent user failures to network routing/packet loss caused by load balancer and NIC misconfiguration, then adding monitoring to prevent recurrence. Also delivers customer-specific Python extensions with strong validation, testing, and backward compatibility.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

Shivani Sharma

Screened

Mid-Level Software Engineer specializing in Cloud, DevOps, and MLOps

Boston, MA3y exp

“Built and productionized a recommendation system from notebook prototype into a low-latency, scalable Cloud Run service using Docker, FastAPI, Terraform, CI/CD (GitHub Actions), and MLOps tooling (Vertex AI, MLflow). Experienced diagnosing real-time workflow issues using structured logging/ELK and GCP metrics, including resolving intermittent 504s by fixing unbounded SQL and adding caching. Also partners with sales/customer teams (Wasabi) to deliver tailored demos, troubleshoot, and drive onboarding/adoption.”

Gitesh Sagvekar

Screened

Mid-level Full-Stack Developer specializing in cloud-native healthcare applications

USA4y exp

“Full-stack engineer with recent experience at Amgen building an internal healthcare data validation/transformation and workflow automation service: Python/FastAPI backend with REST APIs plus a React UI, designed around a canonical contract-first model to handle inconsistent upstream data. Operates production systems on AWS (EC2/ELB/S3/CloudFront) with strong focus on observability (structured logs, correlation IDs) and safe CI/CD-driven migrations; also has experience shipping quickly in ambiguous environments at TCS.”

keerthana s

Screened

Mid-level Backend Software Engineer specializing in Python/FastAPI on AWS

Los Angeles, California4y exp

“Backend engineer with healthcare domain experience building AI-driven radiology workflow systems. Evolved tightly coupled APIs into secure, reliable FastAPI-based services by moving heavy imaging/data processing into idempotent asynchronous pipelines with retries, feature-flagged incremental rollout, and strong data-integrity controls (constraints, backfills, validation). Strong focus on defense-in-depth security for sensitive patient data (OAuth2/JWT, RBAC, and database-level protections).”

Naga Chandra mouli Thammineni

Screened

Mid-level Full-Stack Developer specializing in React and Spring Boot

Hyderabad, Telangana, India2y exp

“Software engineer who built and owned a centralized Asset Management platform at Hyundai Mobis used daily by multiple internal teams, delivering everything from UI/TypeScript React frontend to Spring Boot services, databases, deployment, and post-release support. Experienced in scaling microservices with Kafka-based async workflows, improving data consistency/real-time updates, and implementing reliability patterns like standardized API contracts, retries, DLQs, and targeted caching.”

Nagaveda Sai Kumar Reddy Gajjala

Screened

Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud integrations

Richardson, TX4y exp

“Backend engineer with enterprise SaaS experience (Zoho) who owned an end-to-end cloud integration between Endpoint Central and ServiceDesk Plus, redesigning device onboarding across 64+ scenarios and building a fault-tolerant sync engine that recovered 100% failed transactions. Also built and operated production systems across the stack—FastAPI services with strong testing/observability, React+TypeScript portals, PostgreSQL performance tuning, and AWS deployments with real incident response (RDS CPU saturation resolved with zero downtime).”

Ram Usarty

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native distributed systems

USA4y exp

“Backend/platform-focused engineer who has shipped production LLM agents for messy research dataset submissions, turning manual validation into an automated, reliable ingestion pipeline. Strong on production hardening (streaming large uploads, strict schema/function-calling outputs, idempotency, RBAC) plus eval/monitoring loops that improved data quality, reduced support burden, and increased adoption.”

Srilekha Jakkula

Screened

Senior Data Engineer specializing in scalable data pipelines and API-driven data services

Chicago, IL5y exp

“Data engineer focused on building scalable, reliable end-to-end data pipelines and backend REST data services, spanning API ingestion plus batch/stream processing with Airflow, Kafka, Spark/PySpark, and SQL. Emphasizes strong data quality validation, monitoring/fault tolerance, and performance tuning for large datasets, with experience deploying in cloud environments using containerization and CI/CD.”

“Backend engineer who built a real-time, event-driven alerting platform (Java/Spring Boot, Kafka, MongoDB) processing millions of events per day on AWS (Docker/Kubernetes), including hands-on performance debugging of Kafka consumer lag at peak. Also shipped an end-to-end LLM-based alert summarization feature and designed a multi-step incident triage agent workflow with retries and human-in-the-loop escalation.”

“Backend/microservices engineer (Java/Spring Boot, Kafka, Angular microfrontends) with Teradata experience building distributed analytics/query routing platforms and delivering 20–30% latency reductions through event-driven redesign and reliability hardening. Also built and shipped an end-to-end multimodal medical imaging AI feature (LLaVA/Mistral 7B + LoRA) with production guardrails like confidence-based human review, drift monitoring, and audit logs.”

“Full-stack engineer with production experience across React/TypeScript, Node/Express, and Java/Spring Boot, operating containerized systems on AWS (EKS/ECS/EC2/RDS/S3) with strong observability (CloudWatch/Grafana). Notable for fixing a real checkout/order-placement failure end-to-end by adding frontend submission guards and backend idempotency with Redis + Kafka deduplication, then validating impact via technical metrics and business KPIs. Has also built Kafka-based integrations/pipelines with robust retry/backfill/reconciliation patterns in retail and banking contexts.”

“Backend/real-time systems engineer transitioning into robotics software: building ROS 2 fundamentals (pub/sub, custom messages, launch files) and experimenting with Nav2 + SLAM in Gazebo/RViz. Demonstrated practical debugging by tuning costmaps/planners and analyzing topic latency to stabilize simulated navigation, and has experience integrating telemetry pipelines and REST-based external interfaces.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Backend/integration-focused engineer in the Maryland area with production experience building FastAPI REST services secured with OAuth2.1/JWT and reliability patterns (timeouts, selective retries, idempotency, centralized error handling). Has delivered AWS-integrated MuleSoft/CloudHub solutions and supported AWS Glue ETL workflows, plus demonstrated strong SQL tuning with a 30–40s to 3–5s performance improvement.”

“Software engineer/product owner who has led end-to-end delivery of AI and content-management platforms, including building RAG-based reliability improvements and migrating fragile systems to containerized AWS ECS/Kubernetes with Terraform-managed CI/CD. Experienced designing event-driven microservices (SQS/SNS/RabbitMQ), scaling queue consumers with autoscaling, and creating internal Python tooling to standardize data connectors (e.g., BigQuery/Airtable/internal APIs) to speed iteration.”

“Accenture engineer who owned an insurance e-application end-to-end and drove incremental releases that reduced recurring production issues. Also built a TypeScript/React (Next.js) + NestJS microservices platform using PostgreSQL, Redis, Stripe, and Kafka, with strong focus on decoupling, eventual consistency, and scaling consumers under load. Created a hackathon chat-based internal assistant that used live form context and documentation-grounded answers to help agents resolve customer queries during form filling.”

“Built and shipped a production RAG-based enterprise knowledge assistant to replace slow/inaccurate search across millions of documents, using LangChain orchestration with GPT-4/LLaMA and vector databases. Strong focus on production constraints—latency, hallucination control, and cost—using hybrid retrieval, guardrails, LLM-as-judge validation, and model routing, and has experience translating non-technical stakeholder pain points into measurable outcomes.”

“Senior Unity UI/UX developer who led XR interaction design for a VR meetings/events platform (VRChat/Horizons-like), including a modular radial menu that served as the app’s primary control system. Also built a Pokemon Go-style Unity AR experience for a major shopping centre end-to-end, tackling real-world marker scanning challenges (lighting, reflections, installation) through iterative prototyping and testing.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“Full-stack engineer who built and owned an end-to-end real-time engineering dashboard for Medtronic robotic surgical systems, streaming high-frequency sensor/kinematic data via Python WebSockets to a React/TypeScript UI. Differentiates through performance/reliability practices (stable core vs experimental layer, observability, caching) and high-impact 3D visualization + session playback that became part of engineers' regular bench-testing workflows.”

“Software engineer/product owner experience at GE Healthcare, owning a patient records and claims workflow product end-to-end. Built React/TypeScript + Spring Boot systems with contract-driven APIs (OpenAPI) and operated Spring Boot microservices using RabbitMQ, focusing on reliability patterns (idempotency, DLQs) and performance improvements driven by clinical feedback. Also created an internal monitoring/deployment dashboard that became the default tool for on-call and production support.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”