.png){kind=logo}
Jathin Shettigar
Intern Software Engineer specializing in edge AI deployment and distributed systems
Connect with Jathin
Jathin already has a relationship with Reval, so a warm intro from us gets a much better response than cold outreach.
Recommended
Already have an account?
About
Full-stack engineer who built an enterprise search platform (Codlens) delivering natural-language Q&A over Jira/Slack using embeddings, vector DB search, re-ranking (RRF), and LLM responses with source grounding. Also designed and benchmarked a distributed IAM system with Postgres transaction-log replication and Raft-based quorum consistency, reporting ~253 TPS at ~60ms latency in a multi-node setup. Experience spans early-stage startups (Zetic AI, Sagwara Capital) and large-scale orgs (Akamai, Atlassian).
Find your next great hire
Our AI agents source, screen, and vet candidates for your open roles. Get qualified candidates within 48 hours.
Key Strengths
- Shipped end-to-end enterprise semantic search + Q&A feature (Next.js App Router + FastAPI) integrating Jira/Slack
- Performance optimization via pipeline-level latency measurement (embedding, vector search, LLM stages)
- Vector search tuning (index tuning, limiting search scope) plus caching and embedding reuse to reduce latency
- Improved retrieval relevance using hybrid search with RRF re-ranking
- Designed Postgres schema for distributed IAM (users/roles, transaction log) with constraints and replication-focused indexing
- Optimized replication hot-path query (follower catch-up by sequence number) and validated via query plans + load testing
- Built durable, replayable replication workflow with WAL, idempotent retries, and Raft-based quorum consistency
- Made explicit performance vs consistency tradeoff for sensitive identity data (strong consistency via quorum/raft)
- Built fault-tolerant distributed IAM backend in Go on AWS (Raft-based consistency)
- Debugged and fixed state divergence under network partitions via reproducible failure injection and observability
- Designed readiness gating tied to Raft commit/applied indices to prevent stale reads after recovery
- Shipped end-to-end RAG product (Qdrant + local LLM + FastAPI + React) for cross-system knowledge retrieval
- Strong hallucination-mitigation design: retrieval-first, relevance thresholds, citations, and no-answer fallback
- Designed reliable multi-step LLM agent workflow with structured planning/execution, bounded retries, and escalation/stop conditions
- Improved Postgres ETL performance using EXPLAIN-driven indexing and dbt model refactors (early filtering, incremental materialization)
Like what you see? We'll introduce you to Jathin directly.
Experience
Education
Awards
Languages
Publications
1 publication
Browse Similar
Similar Candidates
Senior Full-Stack Software Engineer specializing in cloud-native distributed systems and AI
Senior AI/ML Engineer specializing in applied AI and scalable backend systems
Staff-level Software Engineer specializing in LLM inference infrastructure and scalable model serving
Senior Full-Stack Engineer specializing in AI/ML platforms and cloud-native systems
Russell Wong
Senior Software Engineer specializing in large-scale backend reliability and media platforms
“Backend/data engineer with experience on large-scale consumer platforms (Google and Meta), building high-traffic Python microservices (REST/gRPC) on Kubernetes with strong reliability/observability practices. Delivered AWS container-based deployments with CI/CD and IaC, and built AWS Glue ETL pipelines on S3 with schema evolution and data quality controls; also has demonstrated SQL tuning impact (15% latency reduction) and incident ownership for batch pipelines.”
Senior AI Engineer specializing in LLMs, RAG, and production ML systems
Interested in Jathin?
We'll personally introduce you - no strings attached.
Build your dream team with Reval
Our AI agents source, screen, and vet candidates for your open roles. Get qualified, high-intent candidates on your desk within 48 hours.
Discover more candidates like Jathin
Search across thousands of pre-screened, high-quality, high-intent candidates on Reval.
Search Talent