.png){kind=logo}
Hiya Kothari
Intern Full-Stack Software Engineer specializing in AI/ML and cloud
Connect with Hiya
Hiya 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
Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.
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
- Designed and evolved geospatial ML backend pipeline for PFAS contamination risk mapping
- Strong focus on reproducibility via versioning, deterministic preprocessing, and experiment parameter/schema logging
- Data quality and integrity engineering (validation checks, quality reports, fail-fast pipelines)
- API performance optimization (cursor-based pagination, indexing, payload limits, moving expensive logic out of request path)
- Safe migration/refactor execution (feature flags, parallel runs, incremental rollout, easy rollback)
- Production-grade security implementation (OAuth2/JWT, role-based access, Postgres RLS policies and testing)
- Improved model accuracy to ~78% and enabled geospatial insights supporting policy discussions
- Shipped AI resume-to-job matching feature end-to-end (embeddings + vector DB + AWS deployment)
- Data-driven iteration using implicit user feedback signals (clicks/skips/manual selections)
- Balanced model tradeoffs via A/B testing across relevance, latency, and cost
- Improved matching precision by 10% and reduced response time by 30% through re-ranking and skill weighting
- Production RAG/LLM pipeline design with robust guardrails (confidence thresholds, PII filtering, fallbacks)
- Empirical threshold calibration using labeled validation sets (precision@k >85% cutoff)
- Built fault-tolerant ingestion pipeline for messy resumes with retries, DLQs, and observability
- Designed continuous LLM eval loops (grounding/hallucination metrics, human review, regression prevention) and drove measurable grounding improvements (+15%)
Like what you see? We'll introduce you to Hiya directly.
Experience
Education
Awards
Skills
Languages
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 Hiya?
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 Hiya
Search across thousands of pre-screened, high-quality, high-intent candidates on Reval.
Search Talent