Headline

SDE 2 | Al & Full-Stack Development | M.S Computer Science with Al

About

I’m a Software Engineer passionate about turning ideas into real-world software that scales. With experience spanning AI-driven systems, full-stack development, and large-scale data engineering, I’ve built solutions that deliver both performance and impact. 📌Explore my profile here: saniyainamdar.netlify.app 🚀At GuardStar, I designed a fault-tolerant Node.js backend and Flutter/React apps powering food-safety platforms, enabling 5,000+ users to receive real-time IoT sensor alerts with <120ms latency. I also created an AI-powered HACCP plan generator using LangChain + RAG pipelines, cutting compliance prep time by 40%. 🚀At Infineon Technologies, I led the SAP BW/4HANA migration, saving €300K annually, and engineered high-performance data workflows with Hadoop, SAP HANA, and ABAP. 🚀As a researcher and open-source contributor, I’ve built GPU-accelerated time series pipelines for EEG classification (HC2, MultiRocket Hydra, Matrix Profile), and developed a DOM element tracking system combining ML, Pinecone, and Neo4j. 🚀Currently completing my MSc in Computer Science (AI major) at the University of Southampton, I work daily with Python, JavaScript/TypeScript, and Java, and thrive at the intersection of scalable system design, machine learning, and user-first engineering. 💡 I love learning fast, solving complex problems creatively, and building software people actually use.

Location

Bengaluru

AI stack

claude code

claude

codex

gemini

langgraph

langsmith

Work experience

SDE 2 at Whatfix, Nov 2025 - Present · 11 mos, Bengaluru, Karnataka, India, End-to-end feature ownership: Designed and shipped Whatfix's Vision Finder with minimal guidance owning API design, LLM integration, distributed system reliability, and production observability from the ground up. Complex interdependent systems: Evolved healing into a structured two-phase pipeline , coordinating across multiple services with a centralised three-tier CSS selector fallback mechanism. Async distributed pipeline: Built a Kafka-driven batch enabling processing of multiple flow items per API call, with session_id-based screenshot sourcing substantially reducing client round-trips in a high-traffic production environment. Distributed concurrency & correctness: Eliminated race conditions across multi-pod deployments by replacing a check-then-write pattern with an update-first upsert and a Redis-backed atomic processing ensuring exactly-once LLM invocation per healing event across pods. Data modelling & pipeline state: Threaded content_type enrichment through the full async pipeline, from OpenAPI codegen through Cassandra staging to all analytics events, enabling per-type segmentation of processing performance in dashboards. Cost controls & configurable thresholds: Introduced Cassandra-backed configurable no-match threshold logic to short-circuit expensive API calls on unresolvable elements; implemented prompt caching reducing unnecessary downstream spend at scale. Observability & structured diagnostics: Integrated OpenTelemetry LLM span tracing (CLIENT spans, OpenInference attributes); introduced a VisionFailureReasonCode enum replacing opaque failures with structured, machine-readable diagnostics surfaced across analytics events. Cross-functional & customer collaboration: Served as technical point of contact on live enterprise customer escalations, diagnosing end-to-end production failures across LLM traces, Cassandra state, and Redis cache, and translating internal system behaviour into clear customer communication.

Software Developer (Ex-CoFounder) at GuardStar Food Safety, Sep 2024 - Oct 2025 · 1 yr 2 mos, Southampton, England, United Kingdom, Designed and implemented a scalable, fault-tolerant full-stack admin panel (Node.js, MongoDB, EC2, Docker) serving 5,000+ concurrent users with p95 latency <120 ms • Developed a mobile application with internationalisation for real-time sensor data visualization and analytics, • Designed and implemented a guided HACCP Plan Generator powered by agentic AI workflows (LangChain + MCP). • Integrated RAG pipelines with Pinecone to allow AI agents to retrieve compliance regulations, reducing manual compliance preparation time by 40%. • Optimised sensor event-processing pipeline using concurrent queues and caching, reducing throughput latency by 43% (from 70 ms to 40 ms) and rendering performance using React memoization, code-splitting, and dynamic imports,reducing page load time by 40%

Business Analyst at Infineon Technologies, Jun 2023 - Aug 2024 · 1 yr 3 mos, Bangalore, Led the in-house migration to BW4HANA System by developing a complete solution without the need for third-party consultations, resulting in significant cost savings and a more tailored implementation. Led 3 major projects in production systems to success by coordinating cross-functional teams and managing project timelines, resulting in on-time delivery and meeting all project objectives. Implemented new features and developed change requests by enhancing various SAP modules (SCM, Purchase, CO, Finance), resulting in improved functionality and user satisfaction. Owned multiple dashboard solutions and confluence by creating and managing user-friendly interfaces, resulting in enhanced data visualization and accessibility for stakeholders. Delivered tech sessions for the AI/ML IT community by preparing and presenting on advanced topics, resulting in knowledge sharing and skill enhancement within the community. Supported the Innovation IT global team by contributing technical expertise and solutions, resulting in the successful implementation of innovative IT strategies.

Intern at Infineon Technologies, Jul 2022 - May 2023 · 11 mos, Bengaluru, Karnataka, India

Machine Learning Intern at Kasetsart University, Jan 2021 - May 2021 · 5 mos, Thailand, This online internship was offered to me by my university. It's a deep learning project. The aim of the project is to detect leaks in water distribution pipelines using sound classification. It's a team of three. While my mates are working on the robotic structure and the ground station, I'm working on the backend(Building an RNN-LSTM model for classifying sound in the water pipeline to detect the distance of the robot from the leakage point). I also drafted a research paper for the project.

Education

Master of Science - MS, Computer Science, University of Southampton

Bachelor of Technology - BTech, Computer Science, Vishwakarma University - VU

Public links

github: https://github.com/CodeFor2001