KA

Kartikey Agarwal

As a Data Scientist, I am driven by a strong ambition to master complex challenges, leveraging my logical approach and creative problem-solving skills to uncover actionable insights. I thrive on autonomous work, constantly seeking innovative ways to apply data science principles and deliver pragmatic solutions that drive tangible impact.

Open to

Delhi

Work style

remote, hybrid

Availability

Available in 60 days

Contact details — on requestProof of Work — on request

Experience

I

Data Scientist

Innovaccer · Oct 2024 – Present

Not yet confirmed
  • Architected a 15-node LangGraph multi-agent orchestration system with model-specific routing logic: Claude Sonnet for clinical reasoning, Claude Opus for medical necessity, Gemini 2.5 Flash for high-throughput parsing via TrueFoundry LLM gateway; achieved 94% ICD-10-CM, CPT, and HCPCS extraction precision benchmarked against 500K+ historical enterprise patient encounters.
  • Defined and engineered an 8-layer hallucination prevention framework: Pydantic structured outputs with mandatory source-citation fields, cross-agent verification passes, rule-based ICD/CPT validators against 200K+ CMS NCCI rules (MongoDB), LangGraph ReAct self-correction loops, and per-agent LLM-judge evaluation tracking precision, recall, F1, and automated hallucination scoring.
  • Designed per-model circuit breakers (5-failure threshold, 60s recovery window, Redis-backed) with exponential backoff and token budget pre-flight guards, cutting failure-cascade recovery time from 210s to under 1s during LLM gateway outages; parallelized independent agents via Temporal workflow orchestration, reducing end-to-end latency 60% (45s to 18s) on 3-page clinical documents.
  • Established production observability across all 15 agents via MLflow tracing: logging token consumption, per-agent latency, and prompt aliases across production and staging environments via Databricks Unity Catalog, enabling precise per-client cost attribution and model resolution auditing.
  • Architected and owned Incurate end-to-end a production clinical NLP platform for unstructured medical documents: OCR → section-aware chunking → LLM entity extraction → semantic code mapping (ICD-10, CPT, RxNorm) → automated evaluation.
  • Fine-tuned MedGemma-27B and Qwen-2.5-7B via LoRA/PEFT on domain-specific clinical datasets, boosting entity extraction F1 from 65% to 94%; selected as the production model after benchmarking against 15+ alternatives.
  • Drove the full-stack migration of the clinical NLP pipeline (OCR + extraction + code mapping) from AWS Comprehend Medical to open-source infrastructure, cutting annual platform costs 94% ($90K to $5K/year).
  • Designed and optimized a Milvus vector search index over 100K+ medical codes (ICD-10, CPT, RxNorm) achieving 91% mapping accuracy, 3x faster than keyword search; PyMuPDF + Vision LLM OCR pipeline achieving 99% accuracy across multi-page clinical documents with section-aware chunking.
  • Architected a microservices backend (FastAPI, AsyncIO, Redis, Celery) reducing inference latency 5.5× for enterprise deployments; system now runs across 3 enterprise healthcare products in production.
  • Benchmarked 15+ LLMs (GPT-40, Gemini 1.5 Pro, LLaMA-3, Qwen-2.5) on entity F1, hallucination rate, and cost-per-1K-tokens; model selection framework adopted as the evaluation standard across the enterprise product stack.
A

Machine Learning Intern

AIMonk Labs · Feb 2023 – Sep 2023

Not yet confirmed
  • Developed an SVM-based customer segmentation model clustering 10K+ visitor profiles (facial expression + visit-pattern features) for personalized retail targeting.
  • Deployed real-time anomaly detection on in-store camera feeds identifying unusual customer movement patterns to surface product placement insights: mapping high-traffic zones to category demand signals, enabling store teams to reduce customer-to-product discovery friction.

Skills 0 proven through work

Also works with

Operational Control LimitsPrompt EngineeringLow-Latency System OptimizationAI Solution DesignCustomer Experience ManagementStore Layout DesignRetail AnalyticsCustomer Behavior AnalysisFacial Expression AnalysisModel Building from ScratchSupport Vector Machine ModelingMachine Learning ApplicationRetry Logic ImplementationError HandlingFault-Tolerant System DesignTemporal Workflow ManagementDocumentation ReviewBusiness Rules VerificationMedical CodingCompliance ManagementSoftware Architecture PrinciplesMulti-Agent System IntegrationAI Hallucination DetectionLangGraph Framework UsageModel Fine-TuningUnstructured Data ProcessingData TransformationMedical Records Review and CompilationMicroservices ArchitectureAI Agent DevelopmentProduct Development

Proof of Work

Proof of Work

Kartikey shares this with people who ask. You'll hear back either way.

Education

BSc (Completed concurrently with B.Tech), Data Science and Applications

Indian Institute of Technology, Madras (IIT-M) · 2021 — 2024

B.Tech (Completed concurrently with BSc), Computer Science and Engineering

Global Institute of Technology, Jaipur (GIT) · 2020 — 2024

Projects

Multi-tier Adaptive Evaluation for Clinical NER Systems

innovaccer.com/resources/white-papers/multi-tier-adaptive-evaluation

SARA: Small Language Models for Clinical Information Extraction in-production

innovaccer.com/resources/white-papers/taming-llms-in-production

Contact details

Contact details

Kartikey shares this with people who ask. You'll hear back either way.

Ask about this candidate

ClaudeChatGPTPerplexity

AI responses may contain errors. Proof status and source information are provided by Proof of Skill.

A Proof CV — one profile, kept current, with the proof attached. Back to the portal