Ayushi Agrawal

Backend Engineer

Bengaluru, Karnataka, India2 yrs 11 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in building scalable, fault-tolerant backend systems.
Proven track record in automating complex workflows.
Strong experience with LLM integration and data processing.

Stackforce AI infers this person is a Backend Engineer specializing in SaaS and Fintech with expertise in scalable architectures.

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Skills

Core Skills

JavaMicroservicesData ProcessingLlm IntegrationRuby On RailsPython

Other Skills

AI AgentsAPI DevelopmentAlgorithmsAmazon Web Services (AWS)Analytical SkillsAngularAngularJSApache FlinkApache KafkaArtificial Intelligence (AI)Attention to DetailBack-End Web DevelopmentBig DataC (Programming Language)C++

About

Java Backend Engineer @ Salesforce | Distributed Systems | Microservices | Cloud | LLM/AI | High-Scale Backend ArchitectureI am a Backend Engineer focused on building scalable, fault-tolerant, cloud-native backend systems using Java , Spring Boot, Microservices, REST/gRPC, Kafka, Redis, Docker/Kubernetes, and modern observability tooling.At Salesforce, I engineer backend platforms that process large-scale unstructured data, power secure access workflows, and integrate LLM-based reasoning systems into production services.🔹 What I work on:• High-throughput PII/Presidio pipelines (250K+ spans)• Distributed microservices with REST/gRPC interfaces• LLM-powered access-decision engines• Policy enforcement & authorization layers• HA services with fault tolerance, retries, circuit breakers• Feature-flag rollouts & zero-downtime deployments• Observability: Splunk • Grafana • Kibana • Tracing🔹 Core Strengths:Java • Spring Boot • Distributed Systems • MicroservicesREST/gRPC • Kafka • Redis • CI/CD • Kubernetes • CloudLLM Integration • Prompt Engineering • API DesignSystem Design (HLD/LLD) • Performance OptimizationScalability • Fault Tolerance • Event-Driven ArchitecturePreviously:EatClub → Ruby on Rails, Angular, Kubernetes, CI/CDJ.P. Morgan (Quant) → Python pipelines, anomaly detection, financial data automation (80% manual effort reduction)I’m passionate about backend architecture, system design, cloud-native engineering, and building reliable production systems that scale.

Experience

2 yrs 11 mos

Total Experience

1 yr 6 mos

Average Tenure

2 yrs 6 mos

Current Experience

Salesforce

Software Development Engineer (Backend)

Nov 2023 – Present · 2 yrs 6 mos · Bengaluru · On-site

As a Java Backend Engineer (SDE-II) at Salesforce, I design and build high-scale, distributed backend systems leveraging Java 11+, Spring Boot, Microservices, REST/gRPC, Kafka, Containerized Deployments, CI/CD, Cloud-Native Architecture, and LLM/AI-driven components. I contribute to the architecture, development, and reliability of enterprise-grade platforms that process large volumes of unstructured data and power compliance, access control, and automation workflows across Salesforce.
I engineer high-throughput data processing pipelines integrated with Microsoft Presidio for advanced PII classification (250K+ spans), including asynchronous flows, parallel execution, secure data transformation, span-level masking, metadata extraction, and policy-driven access control. These pipelines operate as part of a distributed microservice ecosystem, optimized for low latency, fault tolerance, and horizontal scalability.
I designed and implemented an LLM-powered access-decision and troubleshooting engine that performs context extraction, semantic search, rule retrieval, and AI-based reasoning using prompt-engineered templates. This system automates complex decision workflows, reducing manual investigation time by 80%+, and integrates with internal event-driven services, caching layers, and microservice APIs.
I build and maintain core platform components:
REST/gRPC APIs
Interceptors, filters, middleware, and authN/authZ layers
Tag-based policy enforcement engines
High-availability (HA) microservices
Event-driven and async processing modules
These components ensure compliance across distributed systems and reduced unauthorized access incidents by 40%.
I improved service reliability by integrating Splunk (logging), Grafana (metrics), Kibana (search), distributed tracing, and feature flags for safe deployments. I implemented circuit breakers, retry patterns, and rate limiting to strengthen resiliency.

JavaSpring BootMicroservicesREST/gRPCKafkaContainerized Deployments+3

Eatclub brands (formerly box8)

Software Developer

Jun 2023 – Dec 2023 · 6 mos · Bengaluru · On-site

At EatClub, I engineered backend systems and cloud-native operational tools using Ruby on Rails, Angular, Docker, Kubernetes, CI/CD, REST APIs, Microservices, and SQL databases to support large-scale food operations across multiple outlets.
I built backend modules for outlet operations, workforce/resource management, scheduling, order orchestration, and operational workflows, ensuring data consistency, transactional integrity, and reliable API performance. I optimized DB queries, introduced request caching, improved N+1 query patterns, and enhanced concurrency handling.
I improved performance of an Angular-based order management system (OMS) by refining dynamic routing, API integration, state-handling logic, lazy loading, and UI-performance optimization, resulting in reduced latency and smoother real-time updates.
I developed containerized services with Docker, automated deployment pipelines with Jenkins CI/CD, and deployed workloads on Kubernetes with rolling updates, health checks, logging, and monitoring. This improved deployment frequency, reduced downtime, and increased platform stability.
Core technologies:
Ruby on Rails, Angular, REST APIs, Docker, Kubernetes, CI/CD, Jenkins, Microservices, SQL, Caching, Linux, Git, Cloud Deployments.

Ruby on RailsAngularDockerKubernetesCI/CDREST APIs+2

Jpmorgan chase & co.

Quantitative Research Analyst

Jan 2023 – Jun 2023 · 5 mos · Mumbai · On-site

As a Quantitative Research Intern at J.P. Morgan, I built production-grade Python-based analytical pipelines, automation frameworks, financial data processing systems, and visualization tools used for quantitative modeling, PnL reporting, anomaly detection, and analytics.
I developed data ingestion pipelines capable of processing large datasets from internal data stores, applying validation, cleaning, anomaly detection, and transformation logic. I implemented statistical reporting systems using Python, NumPy, Pandas, Matplotlib, and internal visualization frameworks.
I redesigned internal data query frameworks to support modular configuration, multi-LBU integration, and optimized retrieval strategies, improving performance by 20% and reducing redundant computation.
I automated end-to-end PnL reporting workflows, replacing multiple manual processes with automated HTML report generators, Python-based schedulers, error-checking modules, and consistency validation, reducing manual load by 80%.
Core technologies:
Python, Pandas, NumPy, Matplotlib, Data Pipelines, Automation, Statistical Modeling, SQL, Data Cleansing, Anomaly Detection, Time-Series Analysis, HTML Generation, Scripting, Performance Optimization.

PythonPandasNumPyMatplotlibSQLData Pipelines+1