B.Tech., Computer Science and Engineering
SRM University Amaravati, India
Core CS foundations: data structures, algorithms, software engineering, and collaborative project delivery. GPA 9.38/10.
AI & Machine Learning Engineer
Published IEEE Researcher · Entrepreneur in Residence · DeFi-Scale Systems Engineer
Selected signals
About
I'm an engineer who falls in love with the world twice—once through systems and papers, again through trails, coasts, and whatever horizon is next. Raised in India, sharpened at UC Berkeley, I split my life between shipping—venture-scale LLM work, IEEE-cited research, DeFi-grade systems—and stepping outside on purpose, so the work stays honest. Remote-first, globally mobile, Silicon Valley–connected; the passport and the git history both tell a story.
Nature is not decoration here; it is how I think. Long walks, open water, and high country reset the same judgment I bring to diligence and deployment. If the problem matters—agents, backends, research, screens—I'll wear the hat. I'm open to high-impact AI engineering, research, and venture-facing roles anywhere the work is real and the people care about the climb.
$ IMPACT
1,000+
Startups screened
Source: Share Ventures, Dec 2022 – Present
4.7%
VC growth (LLM)
Source: Share Ventures, Dec 2022 – Present
10M+
Pools onboarded
Source: CoinDCX, Sep 2022 – Nov 2022
13M+
Active users
Source: CoinDCX, Sep 2022 – Nov 2022
15+
Engineers on the team
Source: Walmart, Jun 2022 – Jul 2022
6
Founding team
Source: Awair Sense (YC’21), Jan 2021 – Feb 2022
10+
Partners
Source: Awair Sense (YC’21), Jan 2021 – Feb 2022
20+
Hackathon wins
Source: Profile highlights
6+
Research papers
Source: Publications, IEEE and IJCSE
~103
Citations
Source: Publications
9.38
B.Tech GPA out of 10
Source: SRM University Amaravati
3.8
Berkeley GPA out of 4
Source: UC Berkeley Startup Semester
Experience · Education
Share Ventures
AI and data analytics for 1,000+ startup screens; AI Product Manager agents; LLM strategies that boosted VC firm growth 4.7%.
CoinDCX
Protocol-agnostic onboarding for 10M+ pools; DeFi pipeline with load balancing for 13M+ users; validators and node sync.
Walmart
Global software maintenance with JIRA/Confluence; user stories, backlogs, and change orchestration.
Awair Sense (YC’21)
Founding engineer: APIs, MongoDB, AWS/GCP, Unix/Linux, code reviews, 6-person team + 10+ partners.
SRM University Amaravati, India
Core CS foundations: data structures, algorithms, software engineering, and collaborative project delivery. GPA 9.38/10.
University of California, Berkeley, USA
Berkeley entrepreneurship and innovation immersion: product thinking, venture formation, and global competitions. GPA 3.8/4.
$ STACK MAP
OLTP, migrations, indexing, and careful analytical slices.
Flexible documents and iterative schema for product velocity.
Used at Awair Sense (YC’21): APIs, Data, Cloud
Caching, rate limits, sessions, and hot-path acceleration.
Partitioned logs, stream consumers, and replayable events.
Multi-tenant streaming with geo replication options.
Search, observability indices, and text-first retrieval.
Elastic-compatible stacks with open governance models.
Embedded OLAP for local analytics and fast iteration.
Columnar analytics and high-cardinality aggregates.
SaaS warehouse, secure sharing, and elastic compute.
Serverless analytics and petabyte-scale SQL in GCP.
Transformations-as-code and tested warehouse models.
DAG orchestration, SLAs, and data pipeline reliability.
Software-defined assets and stronger data-aware orchestration.
Distributed batch and structured streaming at scale.
Stateful stream processing with event-time correctness.
Async Python APIs, ML microservices, and OpenAPI surfaces.
Batteries-included web apps, admin, and ORM-heavy products.
Lean WSGI apps and gradual complexity where frameworks should stay small.
JavaScript services, tooling, and full-stack glue.
Lightweight HTTP layers and Node microservices.
Structured Node backends with DI and modular boundaries.
Schema-first APIs, loaders, and graph efficiency patterns.
End-to-end typed RPC popular in modern React/Next stacks.
Realtime feeds, collaborative sessions, and event bridges.
Strongly typed RPC between high-throughput internal services.
Versioning, idempotency, pagination, and explicit error contracts.
ASGI workers, lifespan hooks, and hot-reload developer loops.
Process-model WSGI serving for stable Python deployments.
Distributed queues, retries, and long-running background jobs.
Redis-backed Node job queues with observability.
Brokered messaging, routing, and reliable async handoffs.
Durable workflows and saga-style orchestration.
API gateway, plugins, and edge auth rate limits.
Low-latency pub/sub and cloud-native messaging.
Primary language for ML, LLMs, APIs, research, and production services.
Used at Share Ventures: Screening, Automation, Strategy; CoinDCX: Onboarding, Traffic, Nodes; Awair Sense (YC’21): APIs, Data, Cloud
Typed services, SDKs, and maintainable full-stack boundaries.
Node and browser runtimes for product delivery and tooling.
Concurrent services, cloud CLIs, and latency-sensitive network code.
Memory-safe components and performance-critical paths.
Enterprise services, JVM ecosystems, and large-team patterns.
Algorithms, systems thinking, and low-level performance work.
Relational modeling, analytics SQL, migrations, and query tuning.
Automation, packaging, and POSIX deployment scripts.
JVM services, Android-adjacent tooling, and pragmatic typing.
Apple-platform tooling and performance-aware application code.
Systems experiments and predictable low-level control.
Fast scripting, DSLs, and Rails-era integration stacks.
Legacy web surfaces and pragmatic request/response workloads.
Statistical workflows, research charts, and classical analytics.
Numerical computing and scientific performance prototyping.
Portability, edge modules, and browser-adjacent compute.
Training, inference, and research-grade model workflows.
Graphs, TF Serving, and teams standardized on the TF stack.
Composable transforms, XLA-minded research, and high-performance ML.
Transformers, datasets, trainer loops, and hub-driven iteration.
Agents, tools, and retrieval-aware orchestration.
Stateful graphs, checkpoints, and multi-step agent control flow.
Data connectors, indexing, and retrieval pipelines for LLM apps.
Role-based multi-agent coordination for scoped task teams.
Chat, embeddings, evals, and assistant-style product APIs.
Claude-family integrations with safety and tool-use patterns.
High-throughput serving, paging, and batched GPU inference.
Portable graphs and cross-runtime deployment.
NVIDIA-optimized inference and kernel fusion paths.
Multi-framework serving, dynamic batching, and GPU pools.
Classical ML, preprocessing, and fast, interpretable baselines.
Gradient boosting for tabular and ranking workloads.
Distributed training, reinforcement loops, and scalable Python jobs.
Experiment tracking, model registry, and lifecycle hooks.
Metrics, sweeps, artifacts, and regression visibility.
Chunking, embeddings, re-ranking, and grounded answers.
Similarity search at scale and hybrid sparse+dense retrieval.
Postgres-native embeddings and SQL-first retrieval.
Managed vector search with low-latency serving SLAs.
Schema-aware vectors, modules, and multi-tenant patterns.
Large-scale ANN, sharding, and GPU index options.
Lightweight embedding stores for rapid local and prod iteration.
Filtering-rich vector search with strong DevEx and APIs.
Domain adaptation, data curation, and compute-efficient training.
Parameter-efficient adaptation for LLMs under budget.
Preference optimization and human-feedback alignment loops.
Benchmarks, LLM-as-judge, and regression gates before ship.
Policy checks, PII filters, and jailbreak-resistant prompting.
Tool surfaces that connect models to vetted enterprise systems.
Planning, memory, and safe delegation across tools.
Vision+audio+text understanding for richer assistants.
ASR, diarization, and voice UX for hands-free flows.
Image encoders, detection adjacency, and CV+LLM fusion.
Broadcasting, numerics, and array-first scientific computing.
DataFrames, time series, and analyst-friendly transformations.
Lazy, multithreaded frames for fast local and service ETL.
Stats, sparse linear algebra, and classical signal processing.
Publication-style plots and fine-grained axis control.
Expressive statistical visuals atop the PyData stack.
Interactive dashboards and collaborative charting.
Rapid ML demos and stakeholder-facing app shells.
Fast Python package and project management (2025–2026 standard).
Unified lint+format speed for large Python repositories.
Opinionated formatting to end bike-shedding in teams.
Static typing discipline across libraries and services.
Fixtures, parametrization, and fast unit plus integration tests.
Dependency locking and reproducible library workflows.
Modern project tooling for matrices of Python versions.
Hooked checks that keep mainline clean before CI.
Data versioning beside Git for reproducible ML pipelines.
Hot-path acceleration without leaving Python ergonomics.
Exploratory analysis, narrative EDA, and teaching-ready notebooks.
REPL productivity, magics, and debugger ergonomics.
Composable CLIs for data jobs and platform tooling.
Structured, beautiful logs and terminal-native UX.
Compute, IAM, networking, S3-class storage, and managed data adjacency.
Used at Awair Sense (YC’21): APIs, Data, Cloud
BigQuery, GKE, Vertex-adjacent ML, and multi-cloud patterns.
Used at Awair Sense (YC’21): APIs, Data, Cloud
Enterprise identity, hybrid cloud, and Microsoft ecosystem hooks.
Reproducible images, multi-stage builds, and supply-chain hygiene.
Rollouts, autoscaling, probes, and day-two cluster operations.
IaC modules, drift control, and environment parity.
Real-language infra with strong component reuse.
Charted releases and templated Kubernetes deployments.
CI/CD, previews, reusable workflows, and attestations.
Pipelines, security scans, and self-managed runners.
GitOps rollouts with diff views and progressive delivery.
Shell fluency, systemd, perf, and production debugging.
Used at Awair Sense (YC’21): APIs, Data, Cloud
Metrics, exposition formats, and Alertmanager patterns.
Dashboards, on-call visibility, and data source unification.
APM, RUM, synthetics, and unified observability.
Reverse proxy, TLS termination, caching, and static offload.
Component models, hooks, and interactive product surfaces.
SSR/SSG, App Router, edge functions, and SEO-aware apps.
Progressive adoption and approachable component ergonomics.
Compile-time optimizations and small runtime bundles.
Utility-first styling with consistent design tokens.
Preview deploys, edge config, and serverless-adjacent hosting.
Cross-browser E2E with trace viewers and reliable selectors.
Developer-friendly component and integration testing.
Fast Vite-native unit tests with modern DX.
Snapshot and unit coverage across JS/TS codebases.
Isolated UI docs, visual testing, and design QA.
Monorepo task caching and scalable build graphs.
WebGL scenes, instancing, and immersive 3D UX.
Declarative Three.js in React with ecosystem helpers.
Declarative layout animations and shared-element polish.
Lint/format gates that keep style and safety consistent.
Projects
LLM-driven finance analysis: strategy, sentiment, and market-facing tooling.
Automates social campaigns with LLMs—generation, scheduling, and growth workflows.
CRM enhanced with LLM-driven analytics and natural-language interaction.
Content-based image retrieval: features for texture, color, and shape—computer-vision research line.
$ GIT LOG
Publications · Writing
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